From a5e4eef037c4139b92d407bd5358617565846af3 Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Tue, 11 Nov 2025 12:24:05 +0100
Subject: [PATCH 01/10] feat: preserve GraphCellLabel and handle merges in
 to_value GraphCells

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 docling_eval/cvat_tools/cvat_to_docling.py | 81 +++++++++++++++++++---
 1 file changed, 70 insertions(+), 11 deletions(-)

diff --git a/docling_eval/cvat_tools/cvat_to_docling.py b/docling_eval/cvat_tools/cvat_to_docling.py
index 507d6f9..b1f14da 100644
--- a/docling_eval/cvat_tools/cvat_to_docling.py
+++ b/docling_eval/cvat_tools/cvat_to_docling.py
@@ -1761,34 +1761,93 @@ def _process_to_value_relationships(self) -> None:  # noqa: C901
         links: list[GraphLink] = []
         cell_id_seq: int = 0
 
-        def _make_cell(element_id: int, label: GraphCellLabel) -> GraphCell:
+        def _get_merge_group_for_element(element_id: int) -> List[CVATElement]:
+            """Get all elements in the merge group for a given element, if any."""
+            for path_id, element_ids in self.doc_structure.path_mappings.merge.items():
+                if element_id not in element_ids:
+                    continue
+                corrected_ids, _ = self.doc_structure.get_corrected_merge_elements(
+                    path_id, element_ids
+                )
+                return [
+                    e
+                    for el_id in corrected_ids
+                    if (e := self.doc_structure.get_element_by_id(el_id)) is not None
+                ]
+            return []
+
+        def _process_merged_bbox(
+            elements: List[CVATElement],
+        ) -> Tuple[int, str, ProvenanceItem]:
+            """Process merged elements to get union bbox, text, and provenance."""
+            union_bbox = BoundingBox.enclosing_bbox([e.bbox for e in elements])
+            page_no = self._get_page_number_from_bbox(union_bbox)
+            text = self._extract_text_from_bbox(union_bbox, page_no)
+            adjusted_bbox = self._adjust_bbox_for_page(union_bbox, page_no)
+            seg_page = self.segmented_pages[page_no]
+            prov_bbox = adjusted_bbox.to_bottom_left_origin(seg_page.dimension.height)
+            return (
+                page_no,
+                text,
+                ProvenanceItem(
+                    page_no=page_no, bbox=prov_bbox, charspan=(0, len(text))
+                ),
+            )
+
+        def _make_cell(element_id: int, default_label: GraphCellLabel) -> GraphCell:
             nonlocal cell_id_seq
 
-            if element_id in cell_by_element:
+            # Check for existing cell (including merge group members)
+            merge_elements = _get_merge_group_for_element(element_id)
+            if merge_elements:
+                for el in merge_elements:
+                    if el.id in cell_by_element:
+                        cell_by_element[element_id] = cell_by_element[el.id]
+                        return cell_by_element[el.id]
+            elif element_id in cell_by_element:
                 return cell_by_element[element_id]
 
-            element = self.doc_structure.get_element_by_id(element_id)
+            # Get element (if not already in merge_elements)
+            element = (
+                merge_elements[0]
+                if merge_elements
+                else self.doc_structure.get_element_by_id(element_id)
+            )
             if element is None:
                 raise RuntimeError(
                     f"Element {element_id} referenced in to_value path is missing."
                 )
 
-            _, text, prov = self._process_element_bbox(element)
+            # Determine label: preserve GraphCellLabel from CVAT or use default
+            label_source = merge_elements[0] if merge_elements else element
+            cell_label = (
+                label_source.label
+                if isinstance(label_source.label, GraphCellLabel)
+                else default_label
+            )
 
-            item_ref = None
-            node_item = self.element_to_item.get(element_id)
-            if node_item is not None:
-                item_ref = node_item.get_ref()
+            # Process bbox and text (merged or single)
+            if merge_elements:
+                _, text, prov = _process_merged_bbox(merge_elements)
+            else:
+                _, text, prov = self._process_element_bbox(element)
 
+            # Create cell
+            node_item = self.element_to_item.get(element_id)
             cell = GraphCell(
                 cell_id=cell_id_seq,
-                label=label,
+                label=cell_label,
                 text=text,
                 orig=text,
                 prov=prov,
-                item_ref=item_ref,
+                item_ref=node_item.get_ref() if node_item else None,
             )
-            cell_by_element[element_id] = cell
+
+            # Map all merge group members to same cell, or just this element
+            for el_id in (
+                [e.id for e in merge_elements] if merge_elements else [element_id]
+            ):
+                cell_by_element[el_id] = cell
             cell_id_seq += 1
             return cell
 

From 01eac065926fbced0c4256c8c345634d8c7dedb0 Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Tue, 11 Nov 2025 14:20:29 +0100
Subject: [PATCH 02/10] fix: GraphCell deduplication with merge groups

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 docling_eval/cvat_tools/cvat_to_docling.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/docling_eval/cvat_tools/cvat_to_docling.py b/docling_eval/cvat_tools/cvat_to_docling.py
index b1f14da..cd0ab17 100644
--- a/docling_eval/cvat_tools/cvat_to_docling.py
+++ b/docling_eval/cvat_tools/cvat_to_docling.py
@@ -1871,7 +1871,24 @@ def _make_cell(element_id: int, default_label: GraphCellLabel) -> GraphCell:
         if not cell_by_element:
             return
 
-        graph = GraphData(cells=list(cell_by_element.values()), links=links)
+        # Deduplicate cells by cell_id (multiple elements may map to same cell via merges)
+        unique_cells = {cell.cell_id: cell for cell in cell_by_element.values()}
+
+        # Validate all link references exist before creating GraphData
+        valid_cell_ids = set(unique_cells)
+        for link in links:
+            if link.source_cell_id not in valid_cell_ids:
+                raise RuntimeError(
+                    f"Invalid source_cell_id {link.source_cell_id} in GraphLink. "
+                    f"Valid cell_ids: {sorted(valid_cell_ids)}"
+                )
+            if link.target_cell_id not in valid_cell_ids:
+                raise RuntimeError(
+                    f"Invalid target_cell_id {link.target_cell_id} in GraphLink. "
+                    f"Valid cell_ids: {sorted(valid_cell_ids)}"
+                )
+
+        graph = GraphData(cells=list(unique_cells.values()), links=links)
 
         try:
             classify_cells(graph=graph)

From 5b7c7cbe9956cc5d9930a1fc95129881a673c7f3 Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Tue, 11 Nov 2025 14:24:22 +0100
Subject: [PATCH 03/10] feat: Review-bundle app builder

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 .../review_bundle_assets/app.js               | 838 ++++++++++++++++++
 .../review_bundle_assets/index.html           | 105 +++
 .../review_bundle_assets/styles.css           | 488 ++++++++++
 .../campaign_tools/review_bundle_builder.py   | 267 ++++++
 4 files changed, 1698 insertions(+)
 create mode 100644 docling_eval/campaign_tools/review_bundle_assets/app.js
 create mode 100644 docling_eval/campaign_tools/review_bundle_assets/index.html
 create mode 100644 docling_eval/campaign_tools/review_bundle_assets/styles.css
 create mode 100644 docling_eval/campaign_tools/review_bundle_builder.py

diff --git a/docling_eval/campaign_tools/review_bundle_assets/app.js b/docling_eval/campaign_tools/review_bundle_assets/app.js
new file mode 100644
index 0000000..370a4bf
--- /dev/null
+++ b/docling_eval/campaign_tools/review_bundle_assets/app.js
@@ -0,0 +1,838 @@
+const state = {
+  manifest: null,
+  entries: [],
+  visibleEntryIds: [],
+  currentVisibleIndex: 0,
+  decisions: new Map(),
+  activeVisualizationIndex: new Map(),
+  storageHandle: null,
+  localStorageKey: "",
+  editBuffer: null,
+  hasUnsavedChanges: false,
+  persistenceMode: "local",
+  lastImportSource: null,
+};
+
+const elements = {
+  headerTitle: document.getElementById("headerTitle"),
+  queueList: document.getElementById("queueList"),
+  searchInput: document.getElementById("searchInput"),
+  statusFilter: document.getElementById("statusFilter"),
+  entryTitle: document.getElementById("entryTitle"),
+  vizTabs: document.getElementById("vizTabs"),
+  vizFrame: document.getElementById("vizFrame"),
+  commentInput: document.getElementById("commentInput"),
+  decisionButtons: document.querySelectorAll(".status-btn"),
+  clearDecision: document.getElementById("clearDecision"),
+  prevEntry: document.getElementById("prevEntry"),
+  nextEntry: document.getElementById("nextEntry"),
+  reviewCounts: document.getElementById("reviewCounts"),
+  globalCounts: document.getElementById("globalCounts"),
+  connectBundle: document.getElementById("connectBundle"),
+  importButton: document.getElementById("importDecisions"),
+  exportButton: document.getElementById("exportCsv"),
+  importInput: document.getElementById("importInput"),
+  storageStatus: document.getElementById("storageStatus"),
+  storageTarget: document.getElementById("storageTarget"),
+  subsetValue: document.getElementById("subsetValue"),
+  imageValue: document.getElementById("imageValue"),
+  priorityLabel: document.getElementById("priorityLabel"),
+  priorityValue: document.getElementById("priorityValue"),
+  saveDecision: document.getElementById("saveDecision"),
+  discardChanges: document.getElementById("discardChanges"),
+  unsavedNotice: document.getElementById("unsavedNotice"),
+  saveStatus: document.getElementById("saveStatusMessage"),
+};
+
+function createEmptyStatuses() {
+  return USERS.reduce((acc, user) => {
+    acc[user.key] = null;
+    return acc;
+  }, {});
+}
+
+function extractSubmissionName(submissionDir) {
+  if (!submissionDir) {
+    return "";
+  }
+  const normalized = submissionDir.replace(/[/\\]+$/, "");
+  const parts = normalized.split(/[/\\]/).filter(Boolean);
+  return parts[parts.length - 1] ?? "";
+}
+
+function setStorageTarget(mode, description) {
+  state.persistenceMode = mode;
+  elements.storageTarget.textContent = `Saving to: ${description}`;
+}
+
+const STATUS_LABELS = {
+  correct: "Correct",
+  need_changes: "Need changes",
+};
+
+const USERS = [
+  { key: "user_a", label: "User A" },
+  { key: "user_b", label: "User B" },
+];
+
+async function init() {
+  try {
+    const manifest = await fetchJson("manifest.json");
+    setupManifest(manifest);
+    await hydrateDecisions();
+    attachEvents();
+    renderQueue();
+    selectVisibleIndex(0);
+  } catch (error) {
+    elements.globalCounts.textContent =
+      "Unable to load manifest. Serve this folder with `python -m http.server` and reload.";
+    console.error("Failed to initialize review bundle", error);
+  }
+}
+
+function setupManifest(manifest) {
+  state.manifest = manifest;
+  state.entries = manifest.entries;
+  state.visibleEntryIds = state.entries.map((_, index) => index);
+  state.localStorageKey = buildStorageKey(manifest);
+  elements.priorityLabel.textContent = manifest.review_column;
+  const submissionName = extractSubmissionName(manifest.submission_dir);
+  if (submissionName) {
+    elements.headerTitle.textContent = `Docling Review Bundle — ${submissionName}`;
+  }
+  setStorageTarget("local", "Browser storage (connect a bundle to persist review_state.json)");
+  elements.globalCounts.textContent = `${manifest.total_entries} frames queued (sorted by ${manifest.review_column})`;
+}
+
+async function hydrateDecisions() {
+  loadDecisionsFromLocalStorage();
+  try {
+    const fileState = await fetchJson("review_state.json");
+    if (fileState && Array.isArray(fileState.decisions)) {
+      mergeDecisions(fileState.decisions);
+    }
+  } catch (error) {
+    console.warn("No review_state.json found yet", error);
+  }
+  updateCounts();
+}
+
+function buildStorageKey(manifest) {
+  const slugBase = `${manifest.submission_dir}-${manifest.generated_at}-${manifest.total_entries}`;
+  return `docling-review-${slugBase.replace(/[^a-zA-Z0-9]+/g, "-")}`;
+}
+
+function loadDecisionsFromLocalStorage() {
+  if (!state.localStorageKey) {
+    return;
+  }
+  const payload = localStorage.getItem(state.localStorageKey);
+  if (!payload) {
+    return;
+  }
+  try {
+    const parsed = JSON.parse(payload);
+    mergeDecisions(parsed.decisions ?? []);
+  } catch (error) {
+    console.warn("Unable to parse local storage payload", error);
+  }
+}
+
+function persistLocal(decisionsArray) {
+  if (!state.localStorageKey) {
+    return;
+  }
+  const payload = { decisions: decisionsArray };
+  localStorage.setItem(state.localStorageKey, JSON.stringify(payload));
+}
+
+function mergeDecisions(decisionArray) {
+  decisionArray.forEach((decision) => {
+    if (!decision || !decision.entry_id) {
+      return;
+    }
+    const normalized = {
+      ...decision,
+      status_user_a:
+        decision.status_user_a ?? decision.status ?? null,
+      status_user_b:
+        decision.status_user_b ?? decision.status ?? null,
+    };
+    state.decisions.set(decision.entry_id, normalized);
+  });
+}
+
+function initializeEditBuffer(entry, options = {}) {
+  const { preserveStatusMessage = false } = options;
+  const saved = state.decisions.get(entry.entry_id);
+  state.editBuffer = {
+    entry_id: entry.entry_id,
+    statuses: {
+      ...createEmptyStatuses(),
+      user_a: saved?.status_user_a ?? null,
+      user_b: saved?.status_user_b ?? null,
+    },
+    comment: saved?.comment ?? "",
+  };
+  elements.commentInput.value = state.editBuffer.comment ?? "";
+  updateDecisionButtons(state.editBuffer.statuses);
+  setUnsavedChanges(false);
+  if (!preserveStatusMessage) {
+    setSaveStatus();
+  }
+}
+
+function updateDecisionButtons(statuses) {
+  elements.decisionButtons.forEach((button) => {
+    const userKey = button.dataset.user;
+    const userStatus = statuses?.[userKey] ?? null;
+    button.classList.toggle("active", userStatus === button.dataset.status);
+  });
+}
+
+function setUnsavedChanges(hasChanges) {
+  state.hasUnsavedChanges = hasChanges;
+  elements.saveDecision.disabled = !hasChanges;
+  elements.discardChanges.disabled = !hasChanges;
+  elements.unsavedNotice.hidden = !hasChanges;
+  elements.searchInput.disabled = hasChanges;
+  elements.statusFilter.disabled = hasChanges;
+  if (!hasChanges) {
+    elements.unsavedNotice.classList.remove("pulse");
+  } else {
+    setSaveStatus();
+  }
+  updateNavigationButtons();
+}
+
+function showUnsavedWarning() {
+  if (!state.hasUnsavedChanges) {
+    return;
+  }
+  elements.unsavedNotice.hidden = false;
+  elements.unsavedNotice.classList.add("pulse");
+  setTimeout(() => {
+    elements.unsavedNotice.classList.remove("pulse");
+  }, 500);
+}
+
+function setSaveStatus(message, variant = "info") {
+  const el = elements.saveStatus;
+  if (!message) {
+    el.hidden = true;
+    el.textContent = "";
+    el.className = "save-status";
+    return;
+  }
+  el.hidden = false;
+  el.textContent = message;
+  el.className = `save-status ${variant}`;
+}
+
+function extractDecision(decision) {
+  if (!decision) {
+    return {
+      statuses: createEmptyStatuses(),
+      comment: "",
+    };
+  }
+  return {
+    statuses: {
+      ...createEmptyStatuses(),
+      user_a: decision.status_user_a ?? null,
+      user_b: decision.status_user_b ?? null,
+    },
+    comment: decision.comment ?? "",
+  };
+}
+
+function summarizeDecision(decision) {
+  const { statuses, comment } = extractDecision(decision);
+  const statusValues = Object.values(statuses);
+  const hasNeedChanges = statusValues.includes("need_changes");
+  const allCorrect = statusValues.every((value) => value === "correct" && value !== null);
+  const anyStatus = statusValues.some((value) => Boolean(value));
+  const hasComment = Boolean(comment?.trim().length);
+  let label = "Pending";
+  let className = "pending";
+  if (hasNeedChanges) {
+    label = "Needs changes";
+    className = "need_changes";
+  } else if (allCorrect && anyStatus) {
+    label = "All correct";
+    className = "correct";
+  } else if (anyStatus) {
+    label = "Partial review";
+  } else if (hasComment) {
+    label = "Comment only";
+  }
+  return { statuses, comment, label, className };
+}
+
+function attachEvents() {
+  elements.searchInput.addEventListener("input", () => {
+    applyFilters();
+  });
+  elements.statusFilter.addEventListener("change", () => {
+    applyFilters();
+  });
+  elements.prevEntry.addEventListener("click", () => navigate(-1));
+  elements.nextEntry.addEventListener("click", () => navigate(1));
+  elements.decisionButtons.forEach((button) => {
+    button.addEventListener("click", () => {
+      const status = button.dataset.status;
+      const userKey = button.dataset.user;
+      toggleDecisionStatus(userKey, status);
+    });
+  });
+  elements.clearDecision.addEventListener("click", clearDecision);
+  elements.commentInput.addEventListener("input", handleCommentInput);
+  elements.saveDecision.addEventListener("click", saveCurrentDecision);
+  elements.discardChanges.addEventListener("click", discardChanges);
+  elements.connectBundle.addEventListener("click", enableFilePersistence);
+  elements.exportButton.addEventListener("click", exportCsvReport);
+  elements.importButton.addEventListener("click", () => elements.importInput.click());
+  elements.importInput.addEventListener("change", handleImportSelection);
+}
+
+function applyFilters() {
+  if (state.hasUnsavedChanges) {
+    showUnsavedWarning();
+    return;
+  }
+  const term = elements.searchInput.value.trim().toLowerCase();
+  const statusFilter = elements.statusFilter.value;
+  state.visibleEntryIds = state.entries
+    .map((entry, index) => ({ entry, index }))
+    .filter(({ entry }) => filterEntry(entry, term, statusFilter))
+    .map(({ index }) => index);
+  const nextIndex = Math.min(state.currentVisibleIndex, state.visibleEntryIds.length - 1);
+  state.currentVisibleIndex = Math.max(0, nextIndex);
+  renderQueue();
+  if (state.visibleEntryIds.length > 0) {
+    selectVisibleIndex(state.currentVisibleIndex);
+  }
+}
+
+function filterEntry(entry, term, statusFilter) {
+  const matchesTerm = !term
+    || entry.doc_name.toLowerCase().includes(term)
+    || entry.image_name.toLowerCase().includes(term);
+  if (!matchesTerm) {
+    return false;
+  }
+  if (statusFilter === "all") {
+    return true;
+  }
+  const decision = state.decisions.get(entry.entry_id);
+  const summary = summarizeDecision(decision);
+  const statusValues = Object.values(summary.statuses);
+  if (statusFilter === "pending") {
+    return statusValues.some((value) => !value);
+  }
+  if (statusFilter === "correct") {
+    return statusValues.every((value) => value === "correct" && value !== null);
+  }
+  if (statusFilter === "need_changes") {
+    return statusValues.includes("need_changes");
+  }
+  return true;
+}
+
+function renderQueue() {
+  elements.queueList.innerHTML = "";
+  if (!state.visibleEntryIds.length) {
+    elements.queueList.innerHTML = '<p class="empty">No rows match the current filters.</p>';
+    return;
+  }
+  state.visibleEntryIds.forEach((entryIndex, visibleIndex) => {
+    const entry = state.entries[entryIndex];
+    const wrapper = document.createElement("div");
+    wrapper.className = "queue-item";
+    if (visibleIndex === state.currentVisibleIndex) {
+      wrapper.classList.add("active");
+    }
+    const decision = state.decisions.get(entry.entry_id);
+    const summary = summarizeDecision(decision);
+    const reviewMetric = formatPriority(entry.review_value);
+    const userSummary = USERS.map((user) => {
+      const userStatus = summary.statuses[user.key];
+      const label = userStatus ? STATUS_LABELS[userStatus] : "Pending";
+      const className = userStatus ? userStatus : "";
+      return `<span class="user-chip ${className}">${user.label}: ${label}</span>`;
+    }).join("");
+    wrapper.innerHTML = `
+      <p class="title">${entry.doc_name}</p>
+      <p class="subtitle">${entry.image_name}</p>
+      <div class="queue-meta">
+        <span class="review-chip">${state.manifest.review_column}: ${reviewMetric}</span>
+        <span class="badge ${summary.className}">${summary.label}</span>
+      </div>
+      <div class="user-summary">${userSummary}</div>
+    `;
+    wrapper.addEventListener("click", () => {
+      const targetEntryIndex = state.visibleEntryIds[visibleIndex];
+      const currentEntryIndex = state.visibleEntryIds[state.currentVisibleIndex];
+      if (state.hasUnsavedChanges && targetEntryIndex !== currentEntryIndex) {
+        showUnsavedWarning();
+        return;
+      }
+      selectVisibleIndex(visibleIndex);
+    });
+    elements.queueList.appendChild(wrapper);
+  });
+}
+
+function selectVisibleIndex(visibleIndex) {
+  if (!state.visibleEntryIds.length) {
+    return;
+  }
+  state.currentVisibleIndex = Math.max(0, Math.min(visibleIndex, state.visibleEntryIds.length - 1));
+  const entryIndex = state.visibleEntryIds[state.currentVisibleIndex];
+  const entry = state.entries[entryIndex];
+  renderCurrentEntry(entry);
+  renderQueue();
+  updateNavigationButtons();
+}
+
+function updateNavigationButtons() {
+  const locked = state.hasUnsavedChanges;
+  elements.prevEntry.disabled = locked || state.currentVisibleIndex <= 0;
+  elements.nextEntry.disabled = locked || state.currentVisibleIndex >= state.visibleEntryIds.length - 1;
+}
+
+function renderCurrentEntry(entry) {
+  if (!entry) {
+    elements.entryTitle.textContent = "No entries available";
+    state.editBuffer = null;
+    elements.commentInput.value = "";
+    updateDecisionButtons(null);
+    setUnsavedChanges(false);
+    setSaveStatus();
+    return;
+  }
+  elements.entryTitle.textContent = `${entry.doc_name}`;
+  renderEntryInfo(entry);
+  renderVisualization(entry);
+  initializeEditBuffer(entry);
+}
+
+function renderEntryInfo(entry) {
+  const subset = entry.metadata?.subset ?? "—";
+  elements.subsetValue.textContent = subset || "—";
+  elements.imageValue.textContent = entry.image_name ?? "—";
+  elements.priorityValue.textContent = formatPriority(entry.review_value);
+}
+
+function formatPriority(value) {
+  if (value === null || value === undefined || value === "") {
+    return "n/a";
+  }
+  if (typeof value === "number") {
+    return Number.isInteger(value) ? value.toString() : value.toFixed(2);
+  }
+  return value;
+}
+
+function renderVisualization(entry) {
+  const visuals = entry.visualizations ?? [];
+  elements.vizTabs.innerHTML = "";
+  if (!visuals.length) {
+    elements.vizFrame.src = "about:blank";
+    elements.vizFrame.title = "No visualization available";
+    elements.vizFrame.classList.add("empty-frame");
+    elements.vizTabs.innerHTML = '<p class="empty">No visualization HTML files located for this doc.</p>';
+    return;
+  }
+  const storedIndex = state.activeVisualizationIndex.get(entry.entry_id) ?? 0;
+  const activeIndex = Math.min(storedIndex, visuals.length - 1);
+  state.activeVisualizationIndex.set(entry.entry_id, activeIndex);
+  visuals.forEach((visual, index) => {
+    const button = document.createElement("button");
+    button.className = "viz-tab";
+    if (index === activeIndex) {
+      button.classList.add("active");
+    }
+    button.textContent = visual.label;
+    button.addEventListener("click", () => {
+      state.activeVisualizationIndex.set(entry.entry_id, index);
+      renderVisualization(entry);
+    });
+    elements.vizTabs.appendChild(button);
+  });
+  elements.vizFrame.classList.remove("empty-frame");
+  elements.vizFrame.src = visuals[activeIndex].relative_path;
+  elements.vizFrame.title = visuals[activeIndex].label;
+}
+
+function toggleDecisionStatus(userKey, status) {
+  if (!state.editBuffer) {
+    return;
+  }
+  const currentStatus = state.editBuffer.statuses[userKey];
+  const nextStatus = currentStatus === status ? null : status;
+  if (currentStatus === nextStatus) {
+    return;
+  }
+  state.editBuffer.statuses[userKey] = nextStatus;
+  updateDecisionButtons(state.editBuffer.statuses);
+  setUnsavedChanges(true);
+}
+
+function resolveActiveVisualization(entry) {
+  const visuals = entry.visualizations ?? [];
+  if (!visuals.length) {
+    return null;
+  }
+  const index = state.activeVisualizationIndex.get(entry.entry_id) ?? 0;
+  return visuals[Math.min(index, visuals.length - 1)].relative_path;
+}
+
+function handleCommentInput() {
+  if (!state.editBuffer) {
+    return;
+  }
+  const value = elements.commentInput.value;
+  if (state.editBuffer.comment === value) {
+    return;
+  }
+  state.editBuffer.comment = value;
+  setUnsavedChanges(true);
+}
+
+function clearDecision() {
+  if (!state.editBuffer) {
+    return;
+  }
+  state.editBuffer.statuses = createEmptyStatuses();
+  state.editBuffer.comment = "";
+  elements.commentInput.value = "";
+  updateDecisionButtons(state.editBuffer.statuses);
+  setUnsavedChanges(true);
+}
+
+function saveCurrentDecision() {
+  if (!state.editBuffer || !state.hasUnsavedChanges) {
+    return;
+  }
+  const entry = getCurrentEntry();
+  if (!entry) {
+    return;
+  }
+  const statuses = state.editBuffer.statuses;
+  const commentValue = state.editBuffer.comment ?? "";
+  const hasComment = commentValue.trim().length > 0;
+  const hasAnyStatus = USERS.some((user) => statuses[user.key]);
+  let message = "";
+  let variant = "success";
+  if (!hasAnyStatus && !hasComment) {
+    state.decisions.delete(entry.entry_id);
+    message = "Decision cleared.";
+    variant = "muted";
+  } else {
+    const decision = {
+      entry_id: entry.entry_id,
+      doc_name: entry.doc_name,
+      image_name: entry.image_name,
+      review_value: entry.review_value,
+      visualization_path: resolveActiveVisualization(entry),
+      status_user_a: statuses.user_a ?? null,
+      status_user_b: statuses.user_b ?? null,
+      comment: commentValue,
+      updated_at: new Date().toISOString(),
+    };
+    state.decisions.set(entry.entry_id, decision);
+    if (hasAnyStatus) {
+      const parts = USERS.filter((user) => statuses[user.key]).map(
+        (user) => `${user.label}: ${STATUS_LABELS[statuses[user.key]]}`,
+      );
+      message = `Decision saved (${parts.join(" • ")}).`;
+      variant = "success";
+    } else {
+      message = "Comment saved (statuses remain pending).";
+      variant = "info";
+    }
+  }
+  persistDecisions();
+  updateCounts();
+  renderQueue();
+  initializeEditBuffer(entry, { preserveStatusMessage: true });
+  setSaveStatus(message, variant);
+}
+
+function discardChanges() {
+  const entry = getCurrentEntry();
+  if (!entry) {
+    return;
+  }
+  initializeEditBuffer(entry);
+  setSaveStatus("Changes discarded.", "muted");
+}
+
+function getCurrentEntry() {
+  if (!state.visibleEntryIds.length) {
+    return null;
+  }
+  const entryIndex = state.visibleEntryIds[state.currentVisibleIndex];
+  return state.entries[entryIndex];
+}
+
+function navigate(delta) {
+  if (state.hasUnsavedChanges) {
+    showUnsavedWarning();
+    return;
+  }
+  const next = state.currentVisibleIndex + delta;
+  if (next < 0 || next >= state.visibleEntryIds.length) {
+    return;
+  }
+  selectVisibleIndex(next);
+}
+
+function updateCounts() {
+  const totals = USERS.reduce((acc, user) => {
+    acc[user.key] = { correct: 0, need_changes: 0 };
+    return acc;
+  }, {});
+  let reviewed = 0;
+  state.decisions.forEach((decision) => {
+    const { statuses, comment } = extractDecision(decision);
+    const hasAnyStatus = Object.values(statuses).some((value) => Boolean(value));
+    const hasComment = Boolean(comment?.trim().length);
+    if (hasAnyStatus || hasComment) {
+      reviewed += 1;
+    }
+    USERS.forEach((user) => {
+      const userStatus = statuses[user.key];
+      if (userStatus && userStatus in totals[user.key]) {
+        totals[user.key][userStatus] += 1;
+      }
+    });
+  });
+  const total = state.entries.length;
+  const perUser = USERS.map((user) => {
+    const userTotals = totals[user.key];
+    return `${user.label} – Correct ${userTotals.correct}, Need changes ${userTotals.need_changes}`;
+  }).join(" • ");
+  const countsMessage = `Reviewed ${reviewed}/${total} • ${perUser}`;
+  elements.reviewCounts.textContent = countsMessage;
+  elements.globalCounts.textContent = `${total} frames queued • ${reviewed} reviewed`;
+}
+
+function persistDecisions() {
+  const decisionsArray = Array.from(state.decisions.values());
+  persistLocal(decisionsArray);
+  saveToBundle(decisionsArray).catch((error) => {
+    if (error) {
+      console.warn("Unable to write review_state.json", error);
+      elements.storageStatus.textContent =
+        "Connect the bundle directory to enable automatic saving.";
+    }
+  });
+}
+
+async function enableFilePersistence() {
+  if (!window.showDirectoryPicker) {
+    elements.storageStatus.textContent =
+      "Browser does not support the File System Access API. Use Chrome or export CSV manually.";
+    return;
+  }
+  try {
+    const dirHandle = await window.showDirectoryPicker({ mode: "readwrite" });
+    const manifestHandle = await dirHandle.getFileHandle("manifest.json");
+    const manifestFile = await manifestHandle.getFile();
+    const manifestData = JSON.parse(await manifestFile.text());
+    if (manifestData.generated_at !== state.manifest.generated_at) {
+      throw new Error("Selected directory does not match this bundle.");
+    }
+    const stateHandle = await dirHandle.getFileHandle("review_state.json", { create: true });
+    state.storageHandle = stateHandle;
+    elements.storageStatus.textContent = "Connected. Decisions save directly to review_state.json.";
+    elements.connectBundle.textContent = "Change bundle connection";
+    setStorageTarget(
+      "bundle",
+      "review_state.json in the selected bundle directory",
+    );
+    persistDecisions();
+  } catch (error) {
+    console.error("Unable to enable persistence", error);
+    elements.storageStatus.textContent =
+      "Permission denied or wrong folder. Ensure you select the bundle directory.";
+  }
+}
+
+async function saveToBundle(decisionsArray) {
+  if (!state.storageHandle) {
+    return;
+  }
+  const writable = await state.storageHandle.createWritable();
+  await writable.write(JSON.stringify({ decisions: decisionsArray }, null, 2));
+  await writable.close();
+  elements.storageStatus.textContent = `Saved ${decisionsArray.length} items to review_state.json`;
+}
+
+async function fetchJson(path) {
+  const response = await fetch(path, { cache: "no-store" });
+  if (!response.ok) {
+    throw new Error(`Unable to load ${path}`);
+  }
+  return response.json();
+}
+
+function exportCsvReport() {
+  const rows = [
+    [
+      "entry_id",
+      "doc_name",
+      "image_name",
+      "status_user_a",
+      "status_user_b",
+      "comment",
+      "updated_at",
+      "review_value",
+      "visualization_path",
+    ],
+  ];
+  state.decisions.forEach((decision) => {
+    rows.push([
+      decision.entry_id,
+      decision.doc_name,
+      decision.image_name,
+      decision.status_user_a ?? "",
+      decision.status_user_b ?? "",
+      (decision.comment ?? "").replace(/\n/g, " "),
+      decision.updated_at ?? "",
+      decision.review_value ?? "",
+      decision.visualization_path ?? "",
+    ]);
+  });
+  const csvContent = rows.map((cols) => cols.map(escapeCsv).join(",")).join("\n");
+  const blob = new Blob([csvContent], { type: "text/csv" });
+  const link = document.createElement("a");
+  const timestamp = new Date().toISOString().replace(/[:.]/g, "-");
+  link.href = URL.createObjectURL(blob);
+  link.download = `review_log_${timestamp}.csv`;
+  link.click();
+  URL.revokeObjectURL(link.href);
+}
+
+function escapeCsv(value) {
+  const stringValue = value ?? "";
+  if (/,|"|\n/.test(stringValue)) {
+    return `"${stringValue.replace(/"/g, '""')}"`;
+  }
+  return stringValue;
+}
+
+function handleImportSelection(event) {
+  if (state.hasUnsavedChanges) {
+    showUnsavedWarning();
+    event.target.value = "";
+    return;
+  }
+  const [file] = event.target.files ?? [];
+  if (!file) {
+    return;
+  }
+  file
+    .text()
+    .then((text) => {
+      if (file.name.endsWith(".csv")) {
+        importFromCsv(text, file.name);
+      } else {
+        importFromJson(text, file.name);
+      }
+    })
+    .finally(() => {
+      event.target.value = "";
+    });
+}
+
+function importFromJson(jsonText, sourceLabel = "imported file") {
+  try {
+    const payload = JSON.parse(jsonText);
+    if (Array.isArray(payload.decisions)) {
+      mergeDecisions(payload.decisions);
+      persistDecisions();
+      updateCounts();
+      renderQueue();
+      const entry = getCurrentEntry();
+      if (entry) {
+        initializeEditBuffer(entry);
+      }
+      setSaveStatus("Decisions imported.", "info");
+      elements.storageStatus.textContent = `Imported log from ${sourceLabel}. Remember to save or export.`;
+    }
+  } catch (error) {
+    console.error("Unable to parse JSON log", error);
+  }
+}
+
+function importFromCsv(csvText, sourceLabel = "CSV file") {
+  const [headerLine, ...rows] = csvText.trim().split(/\r?\n/);
+  const headers = headerLine.split(",");
+  const hasDualStatus = headers.includes("status_user_a") && headers.includes("status_user_b");
+  const hasLegacyStatus = headers.includes("status");
+  if (!hasDualStatus && !hasLegacyStatus) {
+    console.warn("CSV file missing status columns", headers);
+    return;
+  }
+  const required = hasDualStatus
+    ? ["entry_id", "status_user_a", "status_user_b", "comment"]
+    : ["entry_id", "status", "comment"];
+  if (!required.every((key) => headers.includes(key))) {
+    console.warn("CSV file missing required headers", headers);
+    return;
+  }
+  const entries = rows.map((line) => parseCsvLine(line, headers));
+  const normalizedEntries = entries.map((entry) => {
+    if (hasDualStatus) {
+      return entry;
+    }
+    return {
+      ...entry,
+      status_user_a: entry.status ?? null,
+      status_user_b: entry.status ?? null,
+    };
+  });
+  mergeDecisions(normalizedEntries);
+  persistDecisions();
+  updateCounts();
+  renderQueue();
+  const entry = getCurrentEntry();
+  if (entry) {
+    initializeEditBuffer(entry);
+  }
+  setSaveStatus("Decisions imported.", "info");
+  elements.storageStatus.textContent = `Imported log from ${sourceLabel}. Remember to save or export.`;
+}
+
+function parseCsvLine(line, headers) {
+  const values = [];
+  let current = "";
+  let inQuotes = false;
+  for (let i = 0; i < line.length; i += 1) {
+    const char = line[i];
+    if (char === '"') {
+      if (inQuotes && line[i + 1] === '"') {
+        current += '"';
+        i += 1;
+      } else {
+        inQuotes = !inQuotes;
+      }
+    } else if (char === "," && !inQuotes) {
+      values.push(current);
+      current = "";
+    } else {
+      current += char;
+    }
+  }
+  values.push(current);
+  const record = {};
+  headers.forEach((header, index) => {
+    record[header] = values[index];
+  });
+  return record;
+}
+
+init();
diff --git a/docling_eval/campaign_tools/review_bundle_assets/index.html b/docling_eval/campaign_tools/review_bundle_assets/index.html
new file mode 100644
index 0000000..274f7f6
--- /dev/null
+++ b/docling_eval/campaign_tools/review_bundle_assets/index.html
@@ -0,0 +1,105 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>Docling Review Bundle</title>
+    <link rel="stylesheet" href="styles.css" />
+  </head>
+  <body>
+    <header class="app-header">
+      <h1 id="headerTitle">Docling Review Bundle</h1>
+      <div class="counts" id="globalCounts">Loading manifest…</div>
+    </header>
+    <div class="app-shell">
+      <aside class="queue-panel">
+        <div class="queue-controls">
+          <input id="searchInput" type="search" placeholder="Search doc or image name" />
+          <select id="statusFilter">
+            <option value="all">All rows</option>
+            <option value="pending">Pending only</option>
+            <option value="correct">Correct</option>
+            <option value="need_changes">Need changes</option>
+          </select>
+        </div>
+        <div class="queue-list" id="queueList"></div>
+      </aside>
+      <main class="viewer-panel">
+        <section class="entry-header">
+          <div>
+            <h2 id="entryTitle">Select a document to begin</h2>
+          </div>
+          <div class="entry-actions">
+            <button id="prevEntry" class="ghost">⟵ Prev</button>
+            <button id="nextEntry" class="ghost">Next ⟶</button>
+          </div>
+        </section>
+        <section class="entry-info">
+          <div class="info-chip">
+            <span class="label">Subset</span>
+            <strong id="subsetValue">—</strong>
+          </div>
+          <div class="info-chip">
+            <span class="label">Image</span>
+            <strong id="imageValue">—</strong>
+          </div>
+          <div class="info-chip">
+            <span class="label" id="priorityLabel">Priority</span>
+            <strong id="priorityValue">—</strong>
+          </div>
+          <div class="persistence-buttons top">
+            <button id="connectBundle">Connect bundle for saving</button>
+            <button id="importDecisions" class="ghost">Import log</button>
+            <button id="exportCsv" class="ghost">Export CSV</button>
+          </div>
+        </section>
+        <section class="visualization">
+          <div class="viz-tabs" id="vizTabs"></div>
+          <iframe id="vizFrame" title="Visualization preview" loading="lazy"></iframe>
+        </section>
+        <section class="decision-panel">
+          <div class="user-decision-groups">
+            <div class="user-group" data-user="user_a">
+              <div class="user-heading">User A</div>
+              <div class="decision-buttons">
+                <button data-user="user_a" data-status="correct" class="status-btn success">Correct</button>
+                <button data-user="user_a" data-status="need_changes" class="status-btn warn">Need changes</button>
+              </div>
+            </div>
+            <div class="user-group" data-user="user_b">
+              <div class="user-heading">User B</div>
+              <div class="decision-buttons">
+                <button data-user="user_b" data-status="correct" class="status-btn success">Correct</button>
+                <button data-user="user_b" data-status="need_changes" class="status-btn warn">Need changes</button>
+              </div>
+            </div>
+            <button id="clearDecision" class="ghost">Clear both</button>
+          </div>
+          <label class="comment-field">
+            <span>Comment</span>
+            <textarea id="commentInput" rows="3" placeholder="Add context for this frame"></textarea>
+          </label>
+          <div class="decision-actions">
+            <div class="action-buttons">
+              <button id="saveDecision" class="primary" disabled>Save decision</button>
+              <button id="discardChanges" class="ghost" disabled>Discard changes</button>
+            </div>
+            <p class="unsaved-notice" id="unsavedNotice" hidden>
+              Unsaved changes — save or discard before moving to another sample.
+            </p>
+            <p class="save-status" id="saveStatusMessage" hidden></p>
+          </div>
+        </section>
+        <section class="progress" id="reviewCounts">No review decisions yet.</section>
+        <section class="persistence-panel">
+          <div class="storage-status">
+            <div id="storageTarget"></div>
+            <div id="storageStatus"></div>
+          </div>
+        </section>
+      </main>
+    </div>
+    <input id="importInput" type="file" accept="application/json,text/csv" hidden />
+    <script src="app.js" type="module"></script>
+  </body>
+</html>
diff --git a/docling_eval/campaign_tools/review_bundle_assets/styles.css b/docling_eval/campaign_tools/review_bundle_assets/styles.css
new file mode 100644
index 0000000..b6d2c54
--- /dev/null
+++ b/docling_eval/campaign_tools/review_bundle_assets/styles.css
@@ -0,0 +1,488 @@
+:root {
+  --header-height: 58px;
+  font-family: "Inter", "Segoe UI", system-ui, -apple-system, sans-serif;
+  color: #1d2733;
+  background-color: #f7f8fa;
+}
+
+html,
+body {
+  height: 100%;
+}
+
+body {
+  margin: 0;
+  display: flex;
+  flex-direction: column;
+  overflow: hidden;
+}
+
+.app-header {
+  padding: 0.6rem 1.5rem;
+  background: #14213d;
+  color: #fefefe;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+
+.app-header h1 {
+  margin: 0;
+  font-size: 1.15rem;
+  font-weight: 600;
+}
+
+.app-shell {
+  flex: 1;
+  display: grid;
+  grid-template-columns: 340px 1fr;
+  height: calc(100vh - var(--header-height));
+  min-height: 0;
+  overflow: hidden;
+}
+
+.queue-panel {
+  border-right: 1px solid #d5dae1;
+  background: #fff;
+  display: flex;
+  flex-direction: column;
+  height: 100%;
+  overflow: hidden;
+}
+
+.queue-controls {
+  padding: 1rem;
+  border-bottom: 1px solid #e8ecf2;
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+}
+
+.queue-controls input,
+.queue-controls select {
+  padding: 0.45rem 0.6rem;
+  border: 1px solid #c5ccd6;
+  border-radius: 6px;
+  font-size: 0.95rem;
+}
+
+.queue-list {
+  overflow-y: auto;
+  flex: 1;
+  min-height: 0;
+}
+
+.empty {
+  padding: 1rem;
+  color: #6b7280;
+  font-style: italic;
+}
+
+.queue-item {
+  padding: 0.8rem 1rem;
+  border-bottom: 1px solid #f0f2f5;
+  cursor: pointer;
+  transition: background 0.15s ease;
+}
+
+.queue-item:hover,
+.queue-item.active {
+  background: #ecf2ff;
+}
+
+.queue-item .title {
+  margin: 0;
+  font-size: 0.95rem;
+  font-weight: 600;
+}
+
+.queue-item .subtitle {
+  margin: 0.2rem 0 0;
+  color: #5b6472;
+  font-size: 0.85rem;
+}
+
+.queue-meta {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 0.4rem;
+  margin-top: 0.4rem;
+}
+
+.review-chip {
+  font-size: 0.78rem;
+  color: #1d4ed8;
+  background: rgba(37, 99, 235, 0.12);
+  padding: 0.1rem 0.45rem;
+  border-radius: 999px;
+}
+
+.user-summary {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.3rem;
+  margin-top: 0.35rem;
+}
+
+.user-chip {
+  font-size: 0.75rem;
+  padding: 0.12rem 0.5rem;
+  border-radius: 999px;
+  background: #f3f4f6;
+  color: #374151;
+}
+
+.user-chip.correct {
+  background: #d1fae5;
+  color: #065f46;
+}
+
+.user-chip.need_changes {
+  background: #ffe4e6;
+  color: #b91c1c;
+}
+
+.queue-item .badge {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 0.75rem;
+  padding: 0.1rem 0.45rem;
+  border-radius: 999px;
+}
+
+.badge.pending {
+  background: #f4f6fb;
+  color: #475366;
+}
+
+.badge.correct {
+  background: #d1fae5;
+  color: #065f46;
+}
+
+.badge.need_changes {
+  background: #ffe4e6;
+  color: #b91c1c;
+}
+
+.viewer-panel {
+  padding: 1rem 1.5rem 1.5rem;
+  display: flex;
+  flex-direction: column;
+  gap: 1.2rem;
+  height: 100%;
+  overflow-y: auto;
+}
+
+.entry-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 1rem;
+}
+
+.entry-header h2 {
+  margin: 0;
+  font-size: 1.25rem;
+}
+
+.entry-actions {
+  display: flex;
+  gap: 0.5rem;
+}
+
+.ghost {
+  background: transparent;
+  border: 1px solid #cdd5df;
+  color: #1f2a37;
+  padding: 0.45rem 0.75rem;
+  border-radius: 6px;
+  cursor: pointer;
+}
+
+.ghost:disabled {
+  opacity: 0.4;
+  cursor: not-allowed;
+}
+
+.entry-info {
+  background: #fff;
+  border: 1px solid #e1e6ee;
+  border-radius: 10px;
+  padding: 0.75rem 1rem;
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+  gap: 0.75rem;
+  align-items: flex-start;
+}
+
+.info-chip {
+  display: flex;
+  flex-direction: column;
+  font-size: 0.85rem;
+  color: #4b5563;
+}
+
+.info-chip .label {
+  text-transform: uppercase;
+  font-size: 0.75rem;
+  letter-spacing: 0.04em;
+  color: #9aa3b1;
+}
+
+.info-chip strong {
+  font-size: 0.95rem;
+  color: #111827;
+  word-break: break-word;
+}
+
+.persistence-buttons.top {
+  display: flex;
+  justify-content: flex-end;
+  align-items: center;
+  flex-wrap: wrap;
+  gap: 0.5rem;
+}
+
+.visualization {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+  flex: 1;
+  min-height: 0;
+}
+
+.viz-tabs {
+  display: flex;
+  gap: 0.4rem;
+  flex-wrap: wrap;
+}
+
+.viz-tab {
+  padding: 0.35rem 0.7rem;
+  border-radius: 999px;
+  border: 1px solid #cfd6e2;
+  background: #fff;
+  cursor: pointer;
+  font-size: 0.8rem;
+}
+
+.viz-tab.active {
+  background: #1d4ed8;
+  color: #fff;
+  border-color: #1d4ed8;
+}
+
+#vizFrame {
+  flex: 1;
+  min-height: 0;
+  border: 1px solid #dfe5ef;
+  border-radius: 10px;
+  width: 100%;
+}
+
+#vizFrame.empty-frame {
+  background: repeating-linear-gradient(
+    45deg,
+    rgba(15, 23, 42, 0.03),
+    rgba(15, 23, 42, 0.03) 10px,
+    rgba(15, 23, 42, 0.06) 10px,
+    rgba(15, 23, 42, 0.06) 20px
+  );
+}
+
+.decision-panel {
+  background: #fff;
+  border: 1px solid #e0e5ee;
+  border-radius: 12px;
+  padding: 1rem;
+  display: flex;
+  flex-direction: column;
+  gap: 0.8rem;
+}
+
+.user-decision-groups {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 1rem;
+  align-items: flex-start;
+}
+
+.user-group {
+  background: #f9fafb;
+  border: 1px solid #e2e8f0;
+  border-radius: 8px;
+  padding: 0.75rem;
+  min-width: 200px;
+}
+
+.user-heading {
+  font-size: 0.85rem;
+  font-weight: 600;
+  color: #1f2937;
+  margin-bottom: 0.4rem;
+}
+
+.decision-buttons,
+.action-buttons {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.5rem;
+}
+
+.status-btn {
+  border: none;
+  border-radius: 8px;
+  padding: 0.6rem 1.1rem;
+  cursor: pointer;
+  color: #fff;
+  font-weight: 600;
+  opacity: 0.65;
+  transition: transform 0.1s ease, opacity 0.1s ease;
+}
+
+.primary {
+  background: #1d4ed8;
+  border: 1px solid #1d4ed8;
+  color: #fff;
+  border-radius: 8px;
+  padding: 0.65rem 1.2rem;
+  font-weight: 600;
+  cursor: pointer;
+}
+
+.primary:disabled,
+.status-btn:disabled,
+.ghost:disabled {
+  opacity: 0.55;
+  cursor: not-allowed;
+}
+
+.decision-actions {
+  display: flex;
+  flex-direction: column;
+  gap: 0.4rem;
+}
+
+.unsaved-notice {
+  margin: 0;
+  font-size: 0.9rem;
+  color: #b45309;
+  font-weight: 600;
+}
+
+.unsaved-notice.pulse {
+  animation: pulse 0.5s ease;
+}
+
+.save-status {
+  margin: 0;
+  font-size: 0.9rem;
+  font-weight: 600;
+}
+
+.save-status.success {
+  color: #047857;
+}
+
+.save-status.info {
+  color: #1d4ed8;
+}
+
+.save-status.muted {
+  color: #6b7280;
+}
+
+@keyframes pulse {
+  0% {
+    opacity: 0.4;
+  }
+  100% {
+    opacity: 1;
+  }
+}
+
+.status-btn.success {
+  background: #059669;
+}
+
+.status-btn.warn {
+  background: #c2410c;
+}
+
+.status-btn.active {
+  opacity: 1;
+  transform: translateY(-1px);
+  box-shadow: 0 0 0 2px rgba(14, 165, 233, 0.45);
+}
+
+.comment-field {
+  display: flex;
+  flex-direction: column;
+  gap: 0.3rem;
+}
+
+.comment-field textarea {
+  border-radius: 8px;
+  border: 1px solid #cfd6e2;
+  padding: 0.6rem;
+  font-size: 0.95rem;
+  resize: vertical;
+}
+
+.progress {
+  font-weight: 600;
+}
+
+.persistence-panel {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+}
+
+.persistence-buttons {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.5rem;
+}
+
+.persistence-buttons button {
+  border-radius: 8px;
+  border: 1px solid #1d4ed8;
+  padding: 0.55rem 1rem;
+  background: #1d4ed8;
+  color: #fff;
+  cursor: pointer;
+}
+
+.persistence-buttons .ghost {
+  border-color: #d1d8e6;
+  background: #fff;
+  color: #1f2937;
+}
+
+.storage-status {
+  font-size: 0.9rem;
+  color: #4b5563;
+}
+
+.counts {
+  font-weight: 600;
+  text-align: right;
+}
+
+@media (max-width: 900px) {
+  .app-shell {
+    grid-template-columns: 1fr;
+  }
+
+  .queue-panel {
+    max-height: 40vh;
+  }
+
+  .viewer-panel {
+    padding: 1rem;
+  }
+}
diff --git a/docling_eval/campaign_tools/review_bundle_builder.py b/docling_eval/campaign_tools/review_bundle_builder.py
new file mode 100644
index 0000000..db773f8
--- /dev/null
+++ b/docling_eval/campaign_tools/review_bundle_builder.py
@@ -0,0 +1,267 @@
+"""Utilities to create static review bundles for CVAT submissions."""
+
+from __future__ import annotations
+
+import json
+import shutil
+from dataclasses import asdict, dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Iterable
+
+import pandas as pd
+from pandas.api import types as pd_types
+
+
+@dataclass
+class VisualizationFile:
+    """Structured information about a visualization HTML file."""
+
+    label: str
+    relative_path: str
+
+
+@dataclass
+class ManifestEntry:
+    """Serializable row that backs a single review frame."""
+
+    entry_id: str
+    doc_name: str
+    image_name: str
+    review_value: Any
+    metadata: dict[str, Any]
+    visualizations: list[VisualizationFile]
+
+
+def _read_analysis_sheet(analysis_path: Path, review_column: str) -> pd.DataFrame:
+    if analysis_path.suffix.lower() != ".csv":
+        raise ValueError(
+            f"Analysis file must be a CSV export. Found '{analysis_path.name}'."
+        )
+    dataframe = pd.read_csv(analysis_path)
+    if review_column not in dataframe.columns:
+        available = ", ".join(str(column) for column in dataframe.columns)
+        raise ValueError(
+            f"Column '{review_column}' not present in {analysis_path}. Available columns: {available}."
+        )
+    required_columns = {"doc_name", "image_name"}
+    missing = required_columns.difference({str(column) for column in dataframe.columns})
+    if missing:
+        missing_columns = ", ".join(sorted(missing))
+        raise ValueError(f"Analysis sheet must contain columns {missing_columns}.")
+    review_series = dataframe[review_column]
+    if not pd_types.is_numeric_dtype(review_series):
+        numeric_series = pd.to_numeric(review_series, errors="coerce")
+        if numeric_series.notna().any():
+            dataframe = dataframe.copy()
+            dataframe["__sort_key"] = numeric_series
+            sorted_frame = dataframe.sort_values(
+                by=["__sort_key", review_column],
+                ascending=[False, False],
+                kind="stable",
+            ).drop(columns="__sort_key")
+        else:
+            sorted_frame = dataframe.sort_values(
+                by=review_column, ascending=False, kind="stable"
+            )
+    else:
+        sorted_frame = dataframe.sort_values(
+            by=review_column, ascending=False, kind="stable"
+        )
+    return sorted_frame.reset_index(drop=True)
+
+
+def _stringify_value(value: Any) -> Any:
+    if isinstance(value, float) and pd.isna(value):
+        return None
+    if isinstance(value, (int, float, str, bool)) or value is None:
+        return value
+    return str(value)
+
+
+def _collect_visualizations(submission_dir: Path) -> list[Path]:
+    visualizations: list[Path] = []
+    for html_dir in sorted(submission_dir.glob("*/eval_dataset/visualizations")):
+        visualizations.extend(sorted(html_dir.rglob("*.html")))
+    return visualizations
+
+
+def _select_matches(doc_name: str, html_files: Iterable[Path]) -> list[Path]:
+    raw_name = doc_name.strip()
+    plain_name = Path(raw_name).stem
+    matches: list[Path] = []
+    for html_path in html_files:
+        file_name = html_path.name
+        stem_name = html_path.stem
+        if file_name.startswith(raw_name) or file_name.startswith(plain_name):
+            matches.append(html_path)
+        elif stem_name.startswith(raw_name) or stem_name.startswith(plain_name):
+            matches.append(html_path)
+    return matches
+
+
+def _stage_visualization(
+    html_path: Path,
+    submission_dir: Path,
+    bundle_dir: Path,
+    staged_paths: dict[Path, str],
+) -> str:
+    if html_path in staged_paths:
+        return staged_paths[html_path]
+    if not html_path.is_relative_to(submission_dir):
+        raise ValueError(
+            f"Visualization {html_path} is outside the submission directory {submission_dir}."
+        )
+    relative_source = html_path.relative_to(submission_dir)
+    destination = bundle_dir / "visualizations" / relative_source
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copy2(html_path, destination)
+    relative_path = destination.relative_to(bundle_dir).as_posix()
+    staged_paths[html_path] = relative_path
+    return relative_path
+
+
+def _build_manifest_entries(
+    dataframe: pd.DataFrame,
+    html_files: list[Path],
+    submission_dir: Path,
+    bundle_dir: Path,
+    stage_visualizations: bool,
+    review_column: str,
+) -> list[ManifestEntry]:
+    entries: list[ManifestEntry] = []
+    staged_paths: dict[Path, str] = {}
+    for row_index, (_, row) in enumerate(dataframe.iterrows()):
+        doc_value = row["doc_name"]
+        image_value = row["image_name"]
+        if pd.isna(doc_value) or pd.isna(image_value):
+            raise ValueError(
+                f"Row {row_index} is missing doc_name or image_name. These fields are required."
+            )
+        doc_name = str(doc_value).strip()
+        image_name = str(image_value).strip()
+        if not doc_name or not image_name:
+            raise ValueError(
+                (
+                    "Row "
+                    f"{row_index} contains blank doc_name or image_name. Populate these columns "
+                    "before running the review bundle builder."
+                )
+            )
+        review_value = _stringify_value(row.get(review_column))
+        metadata = {
+            str(column): _stringify_value(row[column]) for column in dataframe.columns
+        }
+        matches = _select_matches(doc_name, html_files)
+        relative_matches = []
+        for match in matches:
+            if stage_visualizations:
+                staged_path = _stage_visualization(
+                    match, submission_dir, bundle_dir, staged_paths
+                )
+                relative_matches.append(
+                    VisualizationFile(label=match.name, relative_path=staged_path)
+                )
+            else:
+                relative_to_submission = match.relative_to(submission_dir)
+                relative_from_bundle = Path("..") / relative_to_submission
+                relative_matches.append(
+                    VisualizationFile(
+                        label=match.name,
+                        relative_path=relative_from_bundle.as_posix(),
+                    )
+                )
+        entry = ManifestEntry(
+            entry_id=f"entry_{row_index:05d}",
+            doc_name=doc_name,
+            image_name=image_name,
+            review_value=review_value,
+            metadata=metadata,
+            visualizations=relative_matches,
+        )
+        entries.append(entry)
+    return entries
+
+
+def _write_json(path: Path, content: Any) -> None:
+    path.write_text(json.dumps(content, ensure_ascii=False, indent=2), encoding="utf-8")
+
+
+def build_review_bundle(
+    submission_dir: Path,
+    review_column: str = "need_review",
+    analysis_filename: str = "combined_evaluation.csv",
+    stage_visualizations: bool = False,
+) -> Path:
+    submission_dir = submission_dir.resolve()
+    if not submission_dir.exists():
+        raise FileNotFoundError(
+            f"Submission directory {submission_dir} does not exist."
+        )
+    analysis_path = submission_dir / analysis_filename
+    if not analysis_path.exists():
+        raise FileNotFoundError(
+            f"Analysis file {analysis_path} not found inside {submission_dir}."
+        )
+    dataframe = _read_analysis_sheet(analysis_path, review_column)
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    bundle_dir = submission_dir / f"review_bundle_{timestamp}"
+    bundle_dir.mkdir(parents=True, exist_ok=False)
+    asset_dir = Path(__file__).with_name("review_bundle_assets")
+    shutil.copytree(asset_dir, bundle_dir, dirs_exist_ok=True)
+    html_files = _collect_visualizations(submission_dir)
+    entries = _build_manifest_entries(
+        dataframe=dataframe,
+        html_files=html_files,
+        submission_dir=submission_dir,
+        bundle_dir=bundle_dir,
+        stage_visualizations=stage_visualizations,
+        review_column=review_column,
+    )
+    manifest = {
+        "generated_at": datetime.now().isoformat(timespec="seconds"),
+        "submission_dir": str(submission_dir),
+        "review_column": review_column,
+        "analysis_file": str(analysis_path),
+        "total_entries": len(entries),
+        "entries": [asdict(entry) for entry in entries],
+    }
+    _write_json(bundle_dir / "manifest.json", manifest)
+    _write_json(bundle_dir / "review_state.json", {"decisions": []})
+    return bundle_dir
+
+
+def main() -> None:
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Build a static review bundle.")
+    parser.add_argument(
+        "submission_dir", type=Path, help="Path to the submission folder"
+    )
+    parser.add_argument(
+        "--review-column",
+        default="need_review",
+        help="Column to sort by in descending order",
+    )
+    parser.add_argument(
+        "--analysis-filename",
+        default="combined_evaluation.csv",
+        help="Analysis CSV filename located inside the submission directory",
+    )
+    parser.add_argument(
+        "--stage-visualizations",
+        action="store_true",
+        help="Copy matching visualization HTML files into the bundle (useful when hosting the bundle alone).",
+    )
+    args = parser.parse_args()
+    bundle_path = build_review_bundle(
+        submission_dir=args.submission_dir,
+        review_column=args.review_column,
+        analysis_filename=args.analysis_filename,
+        stage_visualizations=args.stage_visualizations,
+    )
+    print(f"Review bundle created at {bundle_path}")
+
+
+if __name__ == "__main__":
+    main()

From 159c4a9b4f16e42ee4085ec20a75fbf2b1ee3009 Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Tue, 11 Nov 2025 16:48:00 +0100
Subject: [PATCH 04/10] Update lock

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 uv.lock | 6442 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 3727 insertions(+), 2715 deletions(-)

diff --git a/uv.lock b/uv.lock
index 4f6d72c..0b45afb 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,34 +1,42 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10, <4.0"
 resolution-markers = [
-    "python_full_version >= '3.13' and platform_machine == 'arm64' and sys_platform == 'darwin'",
-    "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'",
-    "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'",
-    "(python_full_version >= '3.13' and platform_machine != 'arm64') or (python_full_version >= '3.13' and sys_platform != 'darwin')",
-    "(python_full_version == '3.12.*' and platform_machine != 'arm64') or (python_full_version == '3.12.*' and sys_platform != 'darwin')",
-    "(python_full_version == '3.11.*' and platform_machine != 'arm64') or (python_full_version == '3.11.*' and sys_platform != 'darwin')",
-    "(python_full_version < '3.11' and platform_machine != 'arm64') or (python_full_version < '3.11' and sys_platform != 'darwin')",
+    "python_full_version >= '3.14' and sys_platform == 'darwin'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.13.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
 name = "accelerate"
-version = "1.10.1"
+version = "1.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "numpy", version = "2.3.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'arm64' or sys_platform != 'darwin'" },
-    { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b1/72/ff3961c19ee395c3d30ac630ee77bfb0e1b46b87edc504d4f83bb4a89705/accelerate-1.10.1.tar.gz", hash = "sha256:3dea89e433420e4bfac0369cae7e36dcd6a56adfcfd38cdda145c6225eab5df8", size = 392446, upload-time = "2025-08-25T13:57:06.21Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/23/60/2757c4f03a8705dbf80b1268b03881927878dca5ed07d74f733fb6c219e0/accelerate-1.11.0.tar.gz", hash = "sha256:bb1caf2597b4cd632b917b5000c591d10730bb024a79746f1ee205bba80bd229", size = 393715, upload-time = "2025-10-20T14:42:25.025Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5f/a0/d9ef19f780f319c21ee90ecfef4431cbeeca95bec7f14071785c17b6029b/accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11", size = 374909, upload-time = "2025-08-25T13:57:04.55Z" },
+    { url = "https://files.pythonhosted.org/packages/77/85/85951bc0f9843e2c10baaa1b6657227056095de08f4d1eea7d8b423a6832/accelerate-1.11.0-py3-none-any.whl", hash = "sha256:a628fa6beb069b8e549460fc449135d5bd8d73e7a11fd09f0bc9fc4ace7f06f1", size = 375777, upload-time = "2025-10-20T14:42:23.256Z" },
 ]
 
 [[package]]
@@ -42,7 +50,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.15"
+version = "3.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -54,76 +62,110 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716, upload-time = "2025-07-29T05:52:32.215Z" }
-wheels = [
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 ]
 
 [[package]]

From 8f1e1981a793d214fffb9eeb0f10425cadb9d1c6 Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Wed, 12 Nov 2025 15:49:26 +0100
Subject: [PATCH 05/10] Docs and updates for review-bundle-builder

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 .../review_bundle_assets/app.js               | 26 +++++++++++++++++--
 .../review_bundle_assets/index.html           |  5 ++++
 .../campaign_tools/review_bundle_builder.py   |  2 +-
 docling_eval/utils/utils.py                   | 23 +++++++++++++---
 4 files changed, 49 insertions(+), 7 deletions(-)

diff --git a/docling_eval/campaign_tools/review_bundle_assets/app.js b/docling_eval/campaign_tools/review_bundle_assets/app.js
index 370a4bf..2c960f9 100644
--- a/docling_eval/campaign_tools/review_bundle_assets/app.js
+++ b/docling_eval/campaign_tools/review_bundle_assets/app.js
@@ -18,6 +18,7 @@ const elements = {
   queueList: document.getElementById("queueList"),
   searchInput: document.getElementById("searchInput"),
   statusFilter: document.getElementById("statusFilter"),
+  vizFilter: document.getElementById("vizFilter"),
   entryTitle: document.getElementById("entryTitle"),
   vizTabs: document.getElementById("vizTabs"),
   vizFrame: document.getElementById("vizFrame"),
@@ -276,6 +277,9 @@ function attachEvents() {
   elements.statusFilter.addEventListener("change", () => {
     applyFilters();
   });
+  elements.vizFilter.addEventListener("change", () => {
+    applyFilters();
+  });
   elements.prevEntry.addEventListener("click", () => navigate(-1));
   elements.nextEntry.addEventListener("click", () => navigate(1));
   elements.decisionButtons.forEach((button) => {
@@ -302,9 +306,10 @@ function applyFilters() {
   }
   const term = elements.searchInput.value.trim().toLowerCase();
   const statusFilter = elements.statusFilter.value;
+  const vizFilter = elements.vizFilter.value;
   state.visibleEntryIds = state.entries
     .map((entry, index) => ({ entry, index }))
-    .filter(({ entry }) => filterEntry(entry, term, statusFilter))
+    .filter(({ entry }) => filterEntry(entry, term, statusFilter, vizFilter))
     .map(({ index }) => index);
   const nextIndex = Math.min(state.currentVisibleIndex, state.visibleEntryIds.length - 1);
   state.currentVisibleIndex = Math.max(0, nextIndex);
@@ -314,13 +319,16 @@ function applyFilters() {
   }
 }
 
-function filterEntry(entry, term, statusFilter) {
+function filterEntry(entry, term, statusFilter, vizFilter) {
   const matchesTerm = !term
     || entry.doc_name.toLowerCase().includes(term)
     || entry.image_name.toLowerCase().includes(term);
   if (!matchesTerm) {
     return false;
   }
+  if (!entryMatchesVisualizationFilter(entry, vizFilter)) {
+    return false;
+  }
   if (statusFilter === "all") {
     return true;
   }
@@ -434,6 +442,20 @@ function formatPriority(value) {
   return value;
 }
 
+function entryMatchesVisualizationFilter(entry, filterValue) {
+  if (filterValue === "all") {
+    return true;
+  }
+  const visuals = entry.visualizations ?? [];
+  if (!visuals.length) {
+    return false;
+  }
+  return visuals.some((visual) => {
+    const target = (visual.label ?? visual.relative_path ?? "").toLowerCase();
+    return target.includes(filterValue);
+  });
+}
+
 function renderVisualization(entry) {
   const visuals = entry.visualizations ?? [];
   elements.vizTabs.innerHTML = "";
diff --git a/docling_eval/campaign_tools/review_bundle_assets/index.html b/docling_eval/campaign_tools/review_bundle_assets/index.html
index 274f7f6..e1542bb 100644
--- a/docling_eval/campaign_tools/review_bundle_assets/index.html
+++ b/docling_eval/campaign_tools/review_bundle_assets/index.html
@@ -21,6 +21,11 @@ <h1 id="headerTitle">Docling Review Bundle</h1>
             <option value="correct">Correct</option>
             <option value="need_changes">Need changes</option>
           </select>
+          <select id="vizFilter">
+            <option value="all">All visualization types</option>
+            <option value="layout">Layout only</option>
+            <option value="key_value">Key-value only</option>
+          </select>
         </div>
         <div class="queue-list" id="queueList"></div>
       </aside>
diff --git a/docling_eval/campaign_tools/review_bundle_builder.py b/docling_eval/campaign_tools/review_bundle_builder.py
index db773f8..b356212 100644
--- a/docling_eval/campaign_tools/review_bundle_builder.py
+++ b/docling_eval/campaign_tools/review_bundle_builder.py
@@ -205,7 +205,7 @@ def build_review_bundle(
         )
     dataframe = _read_analysis_sheet(analysis_path, review_column)
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    bundle_dir = submission_dir / f"review_bundle_{timestamp}"
+    bundle_dir = submission_dir / f"_review_bundle_{timestamp}"
     bundle_dir.mkdir(parents=True, exist_ok=False)
     asset_dir = Path(__file__).with_name("review_bundle_assets")
     shutil.copytree(asset_dir, bundle_dir, dirs_exist_ok=True)
diff --git a/docling_eval/utils/utils.py b/docling_eval/utils/utils.py
index 26ff67c..a512cf0 100644
--- a/docling_eval/utils/utils.py
+++ b/docling_eval/utils/utils.py
@@ -162,12 +162,19 @@ def yield_cells_from_html_table(
 
         num_cols = 0
         for cell in row.find_all(["td", "th"]):
-            num_cols += int(cell.get("colspan", 1))
+            colspan_attr = cell.get("colspan")
+            if isinstance(colspan_attr, str):
+                colspan_val = int(colspan_attr)
+            else:
+                colspan_val = 1
+            num_cols += colspan_val
 
         max_cols = max(max_cols, num_cols)  # Determine maximum columns
 
     # Create grid to track cell positions
-    grid = [[None for _ in range(max_cols)] for _ in range(len(rows))]
+    grid: list[list[Optional[str]]] = [
+        [None for _ in range(max_cols)] for _ in range(len(rows))
+    ]
 
     text_cell_id = 0
     for row_idx, row in enumerate(rows):
@@ -198,8 +205,16 @@ def yield_cells_from_html_table(
                     coord_origin=CoordOrigin.BOTTOMLEFT,
                 )
 
-            rowspan = int(cell.get("rowspan", 1))
-            colspan = int(cell.get("colspan", 1))
+            rowspan_attr = cell.get("rowspan")
+            if isinstance(rowspan_attr, str):
+                rowspan = int(rowspan_attr)
+            else:
+                rowspan = 1
+            colspan_attr = cell.get("colspan")
+            if isinstance(colspan_attr, str):
+                colspan = int(colspan_attr)
+            else:
+                colspan = 1
 
             # Fill grid positions and yield (row, column, text)
             for r in range(rowspan):

From b4e65049967c99c9a42e1a68bf07e290063c7fe1 Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Wed, 12 Nov 2025 17:56:00 +0100
Subject: [PATCH 06/10] fixes for review-bundle-builder and graph-cell merge
 mapping

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 .../campaign_tools/README_review_bundle.md    |  56 ++++++++++
 .../review_bundle_assets/app.js               | 104 +++++++++++++++++-
 .../review_bundle_assets/index.html           |   3 +
 .../campaign_tools/review_bundle_builder.py   |  80 ++++++++++++--
 docling_eval/cvat_tools/cvat_to_docling.py    |  95 +++++++++++++---
 5 files changed, 309 insertions(+), 29 deletions(-)
 create mode 100644 docling_eval/campaign_tools/README_review_bundle.md

diff --git a/docling_eval/campaign_tools/README_review_bundle.md b/docling_eval/campaign_tools/README_review_bundle.md
new file mode 100644
index 0000000..5cc6460
--- /dev/null
+++ b/docling_eval/campaign_tools/README_review_bundle.md
@@ -0,0 +1,56 @@
+# Review Bundle Workflow
+
+Follow these steps whenever you need to review a submission’s visualization outputs.
+
+## 1. Generate the Bundle
+
+You need to start from a submission evaluation directory, e.g. `submission-4_10-11-2025_v0`, as created through the `cvat_evaluation_pipeline.py`.
+It is required to contain a `combined_evaluation.csv` (export it from `combined_evaluation.xlsx` if missing).
+
+Run the bundle builder from the submission root. This reads `combined_evaluation.csv` and writes a timestamped bundle folder next to it. Both `need_review` and `need_task2_review` are included automatically in every bundle, so you can switch between them later without extra arguments.
+
+```bash
+uv run python -m docling_eval.campaign_tools.review_bundle_builder \
+  /path/to/submission-root
+```
+
+The command prints the bundle path, e.g. `submission-4_10-11-2025_v0/review_bundle_20251110_162408`.
+
+### Update an existing bundle in-place
+
+If you already have progress in `review_state.json`, regenerate the manifest and static assets without touching that file by passing `--bundle-dir`. When `--bundle-dir` is supplied you can omit the positional `submission_dir`; the tool automatically resolves it to the bundle’s parent directory:
+
+```bash
+uv run python -m docling_eval.campaign_tools.review_bundle_builder \
+  --bundle-dir /path/to/submission-root/review_bundle_20251110_162408
+```
+
+This keeps `review_state.json` intact while refreshing the HTML/JS/CSS.
+
+## 2. Host the Submission Root
+
+Serve the submission directory so the bundle and original visualization HTMLs share the same origin:
+
+```bash
+cd /path/to/submission-root
+python -m http.server 8765
+```
+
+Open `http://localhost:8765/review_bundle_*/index.html` in your browser.
+
+## 3. Connect `review_state.json`
+
+At the top of the page click **Connect bundle for saving**. Choose the same `review_bundle_*` folder. **This is critical** to ensure the browser has permissions to save into the `review_state.json`, which acts as a database. Once connected, the button switches to “Change bundle connection” and the status line on the bottom states that saves go directly to `review_state.json`. Decisions are still mirrored in browser storage as a safety net.
+
+You can import prior logs via **Import log** (JSON or CSV). Imports do not change the save target — always reconnect if you move to another bundle.
+
+## 4. Review and Save Decisions
+
+The left sidebar lists documents sorted by the configured column. Selecting an entry loads its visualization(s). You will find the tab buttons for key-value and layout if both are available. The decision panel shows separate controls for **User A** and **User B**:
+
+1. Use the **Priority** dropdown above the list to switch between `need_review`, `need_task2_review`, or any other column you exposed during bundle creation.
+2. Click **Correct** or **Need changes** for each user as needed (buttons stay lit when active).
+3. Optionally add a shared comment.
+4. Press **Save decision**. The status line confirms whether you saved user verdicts, comments only, or cleared the entry.
+
+Edits remain locked to the current sample until you save or discard them; this prevents accidental navigation losses. Use **Export CSV** at any time for an external report.
diff --git a/docling_eval/campaign_tools/review_bundle_assets/app.js b/docling_eval/campaign_tools/review_bundle_assets/app.js
index 2c960f9..0ec977b 100644
--- a/docling_eval/campaign_tools/review_bundle_assets/app.js
+++ b/docling_eval/campaign_tools/review_bundle_assets/app.js
@@ -11,6 +11,9 @@ const state = {
   hasUnsavedChanges: false,
   persistenceMode: "local",
   lastImportSource: null,
+  priorityColumns: [],
+  activePriorityColumn: "need_review",
+  sortedEntryIds: null,
 };
 
 const elements = {
@@ -19,6 +22,7 @@ const elements = {
   searchInput: document.getElementById("searchInput"),
   statusFilter: document.getElementById("statusFilter"),
   vizFilter: document.getElementById("vizFilter"),
+  priorityColumnSelect: document.getElementById("priorityColumnSelect"),
   entryTitle: document.getElementById("entryTitle"),
   vizTabs: document.getElementById("vizTabs"),
   vizFrame: document.getElementById("vizFrame"),
@@ -66,6 +70,71 @@ function setStorageTarget(mode, description) {
   elements.storageTarget.textContent = `Saving to: ${description}`;
 }
 
+function derivePriorityColumns(manifest) {
+  if (Array.isArray(manifest.review_columns) && manifest.review_columns.length) {
+    return manifest.review_columns;
+  }
+  if (manifest.review_column) {
+    return [manifest.review_column];
+  }
+  return ["need_review"];
+}
+
+function populatePrioritySelect() {
+  const options = state.priorityColumns
+    .map(
+      (column) => `<option value="${column}">Priority: ${column}</option>`,
+    )
+    .join("");
+  elements.priorityColumnSelect.innerHTML = options;
+  elements.priorityColumnSelect.value = state.activePriorityColumn;
+}
+
+function getReviewValue(entry, column) {
+  if (!entry || !column) {
+    return null;
+  }
+  if (entry.review_values && Object.prototype.hasOwnProperty.call(entry.review_values, column)) {
+    return entry.review_values[column];
+  }
+  if (entry.metadata && Object.prototype.hasOwnProperty.call(entry.metadata, column)) {
+    return entry.metadata[column];
+  }
+  if (column === state.manifest.review_column && Object.prototype.hasOwnProperty.call(entry, "review_value")) {
+    return entry.review_value;
+  }
+  return null;
+}
+
+function normalizeSortValue(value) {
+  if (value === null || value === undefined || value === "") {
+    return Number.NEGATIVE_INFINITY;
+  }
+  if (typeof value === "number") {
+    return value;
+  }
+  const numeric = Number(value);
+  if (!Number.isNaN(numeric)) {
+    return numeric;
+  }
+  return Number.NEGATIVE_INFINITY;
+}
+
+function computeSortedEntryIds(column) {
+  return state.entries
+    .map((entry, index) => ({
+      index,
+      value: normalizeSortValue(getReviewValue(entry, column)),
+    }))
+    .sort((a, b) => {
+      if (b.value !== a.value) {
+        return b.value - a.value;
+      }
+      return a.index - b.index;
+    })
+    .map((item) => item.index);
+}
+
 const STATUS_LABELS = {
   correct: "Correct",
   need_changes: "Need changes",
@@ -102,7 +171,12 @@ function setupManifest(manifest) {
     elements.headerTitle.textContent = `Docling Review Bundle — ${submissionName}`;
   }
   setStorageTarget("local", "Browser storage (connect a bundle to persist review_state.json)");
-  elements.globalCounts.textContent = `${manifest.total_entries} frames queued (sorted by ${manifest.review_column})`;
+  state.priorityColumns = derivePriorityColumns(manifest);
+  state.activePriorityColumn = state.priorityColumns[0];
+  elements.priorityLabel.textContent = state.activePriorityColumn;
+  populatePrioritySelect();
+  state.sortedEntryIds = computeSortedEntryIds(state.activePriorityColumn);
+  elements.globalCounts.textContent = `${manifest.total_entries} frames queued (sorted by ${state.activePriorityColumn})`;
 }
 
 async function hydrateDecisions() {
@@ -280,6 +354,17 @@ function attachEvents() {
   elements.vizFilter.addEventListener("change", () => {
     applyFilters();
   });
+  elements.priorityColumnSelect.addEventListener("change", () => {
+    state.activePriorityColumn = elements.priorityColumnSelect.value;
+    elements.priorityLabel.textContent = state.activePriorityColumn;
+    state.sortedEntryIds = computeSortedEntryIds(state.activePriorityColumn);
+    const entry = getCurrentEntry();
+    if (entry) {
+      renderCurrentEntry(entry);
+    }
+    elements.globalCounts.textContent = `${state.entries.length} frames queued (sorted by ${state.activePriorityColumn})`;
+    applyFilters();
+  });
   elements.prevEntry.addEventListener("click", () => navigate(-1));
   elements.nextEntry.addEventListener("click", () => navigate(1));
   elements.decisionButtons.forEach((button) => {
@@ -307,8 +392,11 @@ function applyFilters() {
   const term = elements.searchInput.value.trim().toLowerCase();
   const statusFilter = elements.statusFilter.value;
   const vizFilter = elements.vizFilter.value;
-  state.visibleEntryIds = state.entries
-    .map((entry, index) => ({ entry, index }))
+  if (!state.sortedEntryIds || !state.sortedEntryIds.length) {
+    state.sortedEntryIds = computeSortedEntryIds(state.activePriorityColumn);
+  }
+  state.visibleEntryIds = state.sortedEntryIds
+    .map((index) => ({ entry: state.entries[index], index }))
     .filter(({ entry }) => filterEntry(entry, term, statusFilter, vizFilter))
     .map(({ index }) => index);
   const nextIndex = Math.min(state.currentVisibleIndex, state.visibleEntryIds.length - 1);
@@ -362,7 +450,9 @@ function renderQueue() {
     }
     const decision = state.decisions.get(entry.entry_id);
     const summary = summarizeDecision(decision);
-    const reviewMetric = formatPriority(entry.review_value);
+    const reviewMetric = formatPriority(
+      getReviewValue(entry, state.activePriorityColumn),
+    );
     const userSummary = USERS.map((user) => {
       const userStatus = summary.statuses[user.key];
       const label = userStatus ? STATUS_LABELS[userStatus] : "Pending";
@@ -373,7 +463,7 @@ function renderQueue() {
       <p class="title">${entry.doc_name}</p>
       <p class="subtitle">${entry.image_name}</p>
       <div class="queue-meta">
-        <span class="review-chip">${state.manifest.review_column}: ${reviewMetric}</span>
+        <span class="review-chip">${state.activePriorityColumn}: ${reviewMetric}</span>
         <span class="badge ${summary.className}">${summary.label}</span>
       </div>
       <div class="user-summary">${userSummary}</div>
@@ -429,7 +519,9 @@ function renderEntryInfo(entry) {
   const subset = entry.metadata?.subset ?? "—";
   elements.subsetValue.textContent = subset || "—";
   elements.imageValue.textContent = entry.image_name ?? "—";
-  elements.priorityValue.textContent = formatPriority(entry.review_value);
+  const value = getReviewValue(entry, state.activePriorityColumn);
+  elements.priorityLabel.textContent = state.activePriorityColumn;
+  elements.priorityValue.textContent = formatPriority(value);
 }
 
 function formatPriority(value) {
diff --git a/docling_eval/campaign_tools/review_bundle_assets/index.html b/docling_eval/campaign_tools/review_bundle_assets/index.html
index e1542bb..6ffab5b 100644
--- a/docling_eval/campaign_tools/review_bundle_assets/index.html
+++ b/docling_eval/campaign_tools/review_bundle_assets/index.html
@@ -26,6 +26,9 @@ <h1 id="headerTitle">Docling Review Bundle</h1>
             <option value="layout">Layout only</option>
             <option value="key_value">Key-value only</option>
           </select>
+          <select id="priorityColumnSelect">
+            <option value="need_review">Priority: need_review</option>
+          </select>
         </div>
         <div class="queue-list" id="queueList"></div>
       </aside>
diff --git a/docling_eval/campaign_tools/review_bundle_builder.py b/docling_eval/campaign_tools/review_bundle_builder.py
index b356212..1e14831 100644
--- a/docling_eval/campaign_tools/review_bundle_builder.py
+++ b/docling_eval/campaign_tools/review_bundle_builder.py
@@ -12,6 +12,8 @@
 import pandas as pd
 from pandas.api import types as pd_types
 
+DEFAULT_EXTRA_REVIEW_COLUMNS = ["need_task2_review"]
+
 
 @dataclass
 class VisualizationFile:
@@ -29,6 +31,7 @@ class ManifestEntry:
     doc_name: str
     image_name: str
     review_value: Any
+    review_values: dict[str, Any]
     metadata: dict[str, Any]
     visualizations: list[VisualizationFile]
 
@@ -71,6 +74,25 @@ def _read_analysis_sheet(analysis_path: Path, review_column: str) -> pd.DataFram
     return sorted_frame.reset_index(drop=True)
 
 
+def _resolve_priority_columns(
+    dataframe: pd.DataFrame,
+    primary_column: str,
+    extra_columns: list[str] | None = None,
+) -> list[str]:
+    if primary_column not in dataframe.columns:
+        available = ", ".join(str(column) for column in dataframe.columns)
+        raise ValueError(
+            f"Column '{primary_column}' not present in analysis data. Available columns: {available}."
+        )
+    columns: list[str] = [primary_column]
+    for column in extra_columns or []:
+        if not column or column in columns:
+            continue
+        if column in dataframe.columns:
+            columns.append(column)
+    return columns
+
+
 def _stringify_value(value: Any) -> Any:
     if isinstance(value, float) and pd.isna(value):
         return None
@@ -127,7 +149,7 @@ def _build_manifest_entries(
     submission_dir: Path,
     bundle_dir: Path,
     stage_visualizations: bool,
-    review_column: str,
+    review_columns: list[str],
 ) -> list[ManifestEntry]:
     entries: list[ManifestEntry] = []
     staged_paths: dict[Path, str] = {}
@@ -148,7 +170,12 @@ def _build_manifest_entries(
                     "before running the review bundle builder."
                 )
             )
-        review_value = _stringify_value(row.get(review_column))
+        review_values = {
+            column: _stringify_value(row.get(column))
+            for column in review_columns
+            if column in dataframe.columns
+        }
+        primary_value = review_values.get(review_columns[0])
         metadata = {
             str(column): _stringify_value(row[column]) for column in dataframe.columns
         }
@@ -175,7 +202,8 @@ def _build_manifest_entries(
             entry_id=f"entry_{row_index:05d}",
             doc_name=doc_name,
             image_name=image_name,
-            review_value=review_value,
+            review_value=primary_value,
+            review_values=review_values,
             metadata=metadata,
             visualizations=relative_matches,
         )
@@ -192,6 +220,8 @@ def build_review_bundle(
     review_column: str = "need_review",
     analysis_filename: str = "combined_evaluation.csv",
     stage_visualizations: bool = False,
+    extra_review_columns: list[str] | None = None,
+    bundle_dir: Path | None = None,
 ) -> Path:
     submission_dir = submission_dir.resolve()
     if not submission_dir.exists():
@@ -204,9 +234,23 @@ def build_review_bundle(
             f"Analysis file {analysis_path} not found inside {submission_dir}."
         )
     dataframe = _read_analysis_sheet(analysis_path, review_column)
+    extra_review_columns = (
+        DEFAULT_EXTRA_REVIEW_COLUMNS
+        if extra_review_columns is None
+        else extra_review_columns
+    )
+    priority_columns = _resolve_priority_columns(
+        dataframe=dataframe,
+        primary_column=review_column,
+        extra_columns=extra_review_columns,
+    )
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    bundle_dir = submission_dir / f"_review_bundle_{timestamp}"
-    bundle_dir.mkdir(parents=True, exist_ok=False)
+    if bundle_dir is None:
+        bundle_dir = submission_dir / f"_review_bundle_{timestamp}"
+        bundle_dir.mkdir(parents=True, exist_ok=False)
+    else:
+        bundle_dir = bundle_dir.resolve()
+        bundle_dir.mkdir(parents=True, exist_ok=True)
     asset_dir = Path(__file__).with_name("review_bundle_assets")
     shutil.copytree(asset_dir, bundle_dir, dirs_exist_ok=True)
     html_files = _collect_visualizations(submission_dir)
@@ -216,18 +260,21 @@ def build_review_bundle(
         submission_dir=submission_dir,
         bundle_dir=bundle_dir,
         stage_visualizations=stage_visualizations,
-        review_column=review_column,
+        review_columns=priority_columns,
     )
     manifest = {
         "generated_at": datetime.now().isoformat(timespec="seconds"),
         "submission_dir": str(submission_dir),
         "review_column": review_column,
+        "review_columns": priority_columns,
         "analysis_file": str(analysis_path),
         "total_entries": len(entries),
         "entries": [asdict(entry) for entry in entries],
     }
     _write_json(bundle_dir / "manifest.json", manifest)
-    _write_json(bundle_dir / "review_state.json", {"decisions": []})
+    review_state_path = bundle_dir / "review_state.json"
+    if not review_state_path.exists():
+        _write_json(review_state_path, {"decisions": []})
     return bundle_dir
 
 
@@ -236,7 +283,10 @@ def main() -> None:
 
     parser = argparse.ArgumentParser(description="Build a static review bundle.")
     parser.add_argument(
-        "submission_dir", type=Path, help="Path to the submission folder"
+        "submission_dir",
+        type=Path,
+        nargs="?",
+        help="Path to the submission folder (optional when --bundle-dir is provided)",
     )
     parser.add_argument(
         "--review-column",
@@ -253,12 +303,24 @@ def main() -> None:
         action="store_true",
         help="Copy matching visualization HTML files into the bundle (useful when hosting the bundle alone).",
     )
+    parser.add_argument(
+        "--bundle-dir",
+        type=Path,
+        help="Optional existing bundle directory to update in place.",
+    )
     args = parser.parse_args()
+    submission_dir = args.submission_dir
+    bundle_dir = args.bundle_dir
+    if submission_dir is None:
+        if bundle_dir is None:
+            parser.error("You must provide either submission_dir or --bundle-dir.")
+        submission_dir = bundle_dir.resolve().parent
     bundle_path = build_review_bundle(
-        submission_dir=args.submission_dir,
+        submission_dir=submission_dir,
         review_column=args.review_column,
         analysis_filename=args.analysis_filename,
         stage_visualizations=args.stage_visualizations,
+        bundle_dir=bundle_dir,
     )
     print(f"Review bundle created at {bundle_path}")
 
diff --git a/docling_eval/cvat_tools/cvat_to_docling.py b/docling_eval/cvat_tools/cvat_to_docling.py
index cd0ab17..b48a71a 100644
--- a/docling_eval/cvat_tools/cvat_to_docling.py
+++ b/docling_eval/cvat_tools/cvat_to_docling.py
@@ -1761,20 +1761,73 @@ def _process_to_value_relationships(self) -> None:  # noqa: C901
         links: list[GraphLink] = []
         cell_id_seq: int = 0
 
-        def _get_merge_group_for_element(element_id: int) -> List[CVATElement]:
-            """Get all elements in the merge group for a given element, if any."""
-            for path_id, element_ids in self.doc_structure.path_mappings.merge.items():
-                if element_id not in element_ids:
-                    continue
-                corrected_ids, _ = self.doc_structure.get_corrected_merge_elements(
-                    path_id, element_ids
+        # Build key/value merge group mapping: only merge key/value elements that share same parent
+        from docling_core.types.doc.labels import DocItemLabel, GraphCellLabel
+
+        from docling_eval.cvat_tools.utils import (
+            DEFAULT_PROXIMITY_THRESHOLD,
+            get_deepest_element_at_point,
+        )
+
+        element_to_keyvalue_merge_group: Dict[int, Set[int]] = {}
+
+        for path_id, _ in self.doc_structure.path_mappings.merge.items():
+            merge_path = next(
+                (p for p in self.doc_structure.paths if p.id == path_id), None
+            )
+            if not merge_path:
+                continue
+
+            # Find touched elements (including GraphCellLabel)
+            touched = {
+                el.id: el
+                for pt in merge_path.points
+                if (
+                    el := get_deepest_element_at_point(
+                        pt,
+                        self.doc_structure.elements,
+                        DEFAULT_PROXIMITY_THRESHOLD,
+                        skip_graph_cells=False,
+                    )
                 )
-                return [
-                    e
-                    for el_id in corrected_ids
-                    if (e := self.doc_structure.get_element_by_id(el_id)) is not None
-                ]
-            return []
+            }
+            touched_elements = list(touched.values())
+
+            # Must be same GraphCellLabel type (all KEY or all VALUE) and share same parent
+            if (
+                len(touched_elements) > 1
+                and all(isinstance(el.label, GraphCellLabel) for el in touched_elements)
+                and len({el.label for el in touched_elements}) == 1
+            ):
+                # Find parent IDs
+                parent_ids = {
+                    next(
+                        (
+                            p.id
+                            for p in self.doc_structure.elements
+                            if p.label in [DocItemLabel.LIST_ITEM, DocItemLabel.TEXT]
+                            and bbox_contains(el.bbox, p.bbox, threshold=0.5)
+                        ),
+                        None,
+                    )
+                    for el in touched_elements
+                }
+
+                if len(parent_ids) == 1 and None not in parent_ids:
+                    touched_group = set(touched.keys())
+                    for el_id in touched_group:
+                        element_to_keyvalue_merge_group[el_id] = touched_group
+
+        def _get_merge_group_for_element(element_id: int) -> List[CVATElement]:
+            merge_group_ids = element_to_keyvalue_merge_group.get(element_id)
+            if not merge_group_ids:
+                return []
+            merge_elements = [
+                e
+                for el_id in merge_group_ids
+                if (e := self.doc_structure.get_element_by_id(el_id)) is not None
+            ]
+            return merge_elements if len(merge_elements) > 1 else []
 
         def _process_merged_bbox(
             elements: List[CVATElement],
@@ -1829,11 +1882,25 @@ def _make_cell(element_id: int, default_label: GraphCellLabel) -> GraphCell:
             # Process bbox and text (merged or single)
             if merge_elements:
                 _, text, prov = _process_merged_bbox(merge_elements)
+                _logger.debug(
+                    f"Processing merged cell for element {element_id} with {len(merge_elements)} "
+                    f"merged elements: {[e.id for e in merge_elements]}"
+                )
             else:
                 _, text, prov = self._process_element_bbox(element)
 
             # Create cell
-            node_item = self.element_to_item.get(element_id)
+            # For merged elements, try to get node_item from any merged element
+            node_item = None
+            if merge_elements:
+                # Try to find node_item from any merged element
+                for merged_el in merge_elements:
+                    node_item = self.element_to_item.get(merged_el.id)
+                    if node_item is not None:
+                        break
+            else:
+                node_item = self.element_to_item.get(element_id)
+
             cell = GraphCell(
                 cell_id=cell_id_seq,
                 label=cell_label,

From 18379c9c0b632f239d46fe14306fa8103a6d1daf Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Thu, 13 Nov 2025 15:01:41 +0100
Subject: [PATCH 07/10] Eliminate need for page_imgs in
 cvat_dataset_preannotated, remove json_groundtruth

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 docling_eval/cvat_tools/folder_parser.py      | 35 +++++++++++--------
 .../cvat_preannotation_builder.py             | 34 +++++++++++++-----
 2 files changed, 46 insertions(+), 23 deletions(-)

diff --git a/docling_eval/cvat_tools/folder_parser.py b/docling_eval/cvat_tools/folder_parser.py
index 0c59bec..74516e2 100644
--- a/docling_eval/cvat_tools/folder_parser.py
+++ b/docling_eval/cvat_tools/folder_parser.py
@@ -88,22 +88,27 @@ def parse_pages_from_xml(
         doc_hash = extract_doc_hash_from_filename(image_name)
         page_number = extract_page_number_from_filename(image_name)
 
-        page_imgs_path = assets_root / "page_imgs" / image_name
-
+        # Prefer cvat_tasks/ over page_imgs/ to eliminate redundancy
+        # (images are identical, cvat_tasks/ is always created for CVAT upload)
         image_path: Optional[Path] = None
-        if page_imgs_path.exists():
-            image_path = page_imgs_path
-        else:
-            task_dir_candidates = [
-                tasks_dir / f"task_{task_id:02d}",
-                assets_root / "cvat_tasks" / f"task_{task_id:02d}",
-                xml_path.parent,
-            ]
-            for candidate_dir in task_dir_candidates:
-                candidate_file = candidate_dir / image_name
-                if candidate_file.exists():
-                    image_path = candidate_file
-                    break
+
+        # First, check cvat_tasks/ directories
+        task_dir_candidates = [
+            tasks_dir / f"task_{task_id:02d}",
+            assets_root / "cvat_tasks" / f"task_{task_id:02d}",
+            xml_path.parent,
+        ]
+        for candidate_dir in task_dir_candidates:
+            candidate_file = candidate_dir / image_name
+            if candidate_file.exists():
+                image_path = candidate_file
+                break
+
+        # Fallback to page_imgs/ for backward compatibility with existing datasets
+        if image_path is None:
+            page_imgs_path = assets_root / "page_imgs" / image_name
+            if page_imgs_path.exists():
+                image_path = page_imgs_path
 
         if image_path is None:
             raise FileNotFoundError(f"Image file not found for {image_name}")
diff --git a/docling_eval/dataset_builders/cvat_preannotation_builder.py b/docling_eval/dataset_builders/cvat_preannotation_builder.py
index 7d2ef60..8fb9643 100644
--- a/docling_eval/dataset_builders/cvat_preannotation_builder.py
+++ b/docling_eval/dataset_builders/cvat_preannotation_builder.py
@@ -2,6 +2,7 @@
 import json
 import logging
 import os
+import shutil
 from pathlib import Path
 from typing import Dict, List, Union
 
@@ -48,6 +49,7 @@ def __init__(
         bucket_size: int = 200,
         use_predictions: bool = False,
         sliding_window: int = 2,
+        cleanup_json_groundtruth_after_creation: bool = True,
     ):
         """
         Initialize the CvatPreannotationBuilder.
@@ -58,6 +60,7 @@ def __init__(
             bucket_size: Number of documents per bucket for CVAT tasks
             use_predictions: Whether to use predictions instead of ground truth
             sliding_window: Size of sliding window for page processing (1 for single pages, >1 for multi-page windows)
+            cleanup_json_groundtruth_after_creation: Whether to delete json_groundtruth/ after preannotation XML generation (default True)
         """
         self.source_dir = dataset_source
         self.target_dir = target
@@ -69,6 +72,9 @@ def __init__(
         )
         self.overview = AnnotationOverview()
         self.use_predictions = use_predictions
+        self.cleanup_json_groundtruth_after_creation = (
+            cleanup_json_groundtruth_after_creation
+        )
 
     def _relative_to_target(self, path: Union[Path, str]) -> Path:
         """Return a path relative to the target directory when possible."""
@@ -544,6 +550,16 @@ def _create_preannotation_files(self, sliding_window: int = 2) -> None:
         for bucket_id, annotations in bucket_annotations.items():
             self._write_preannotation_file(bucket_id, annotations)
 
+        # Cleanup json_groundtruth/ after preannotation XML generation completes
+        # (it's only needed during creation, not for evaluation or CVAT upload)
+        if self.cleanup_json_groundtruth_after_creation:
+            json_groundtruth_dir = self.benchmark_dirs.json_true_dir
+            if json_groundtruth_dir.exists():
+                shutil.rmtree(json_groundtruth_dir)
+                _log.info(
+                    f"Cleaned up {json_groundtruth_dir} after preannotation creation"
+                )
+
         # Save overview with all the properly set file paths
         self.overview.save_as_json(self.benchmark_dirs.overview_file)
         _log.info(
@@ -667,14 +683,15 @@ def _process_pages_in_the_document_with_sliding_window(
                 page_no for page_no in range(page_start, page_end)
             ]
 
-            # Save individual page images
+            # Save individual page images to cvat_tasks/ in PNG format (needed for CVAT uploader)
+            # This eliminates redundancy with page_imgs/ directory
             annotated_image.page_img_files = []
             for page_no in range(page_start, page_end):
                 # Create unique filename for the page image
                 page_filename = f"doc_{doc_hash}_page_{page_no:06}.png"
 
-                # Save page image to both task directory and page images directory
-                page_img_file = self.benchmark_dirs.page_imgs_dir / page_filename
+                # Save page image to task directory
+                page_img_file = bucket_dir / page_filename
                 annotated_image.page_img_files.append(page_img_file)
 
                 _log.info(f"saving {page_img_file}")
@@ -749,18 +766,19 @@ def _process_each_page_in_the_document(
                 img_file=bucket_dir / filename,
             )
 
-            # Save page image to both task directory and page images directory
-            page_img_file = self.benchmark_dirs.page_imgs_dir / filename
-            annotated_image.page_img_files = [page_img_file]
-
             # Extract and save page image
             page_image_ref = page.image
             if page_image_ref is not None:
                 page_image = page_image_ref.pil_image
 
                 if page_image is not None:
+                    # Always save images to cvat_tasks/ in PNG format (needed for CVAT uploader)
+                    # This applies to both PDF and image inputs
                     page_image.save(str(annotated_image.img_file))
-                    page_image.save(str(annotated_image.page_img_files[0]))
+
+                    # Point page_img_files to cvat_tasks location (same as img_file)
+                    # This eliminates redundancy with page_imgs/ directory
+                    annotated_image.page_img_files = [annotated_image.img_file]
 
                     annotated_image.img_w = page_image.width
                     annotated_image.img_h = page_image.height

From ef309f1bdf67488ca5825dacd353cd561923d8bc Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Thu, 13 Nov 2025 16:15:35 +0100
Subject: [PATCH 08/10] refactor: modernize CVAT dataset builder and improve
 path handling

Replace legacy CVAT annotation processing code (~1400 lines) with modern
convert_cvat_folder_to_docling() converter in CvatDatasetBuilder. This
significantly simplifies the codebase and aligns with the modern CVAT
converter architecture.

- Remove legacy annotation parsing and document creation methods
- Use convert_cvat_folder_to_docling() for CVAT-to-Docling conversion
- Improve path handling in CvatPreannotationBuilder for moved datasets
- Remove unused original and original_prediction storage in json_dataset_joiner
- Update test data annotations to match new converter output

BREAKING CHANGE: CvatDatasetBuilder now requires modern CVAT folder
structure and uses convert_cvat_folder_to_docling() internally.

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 docling_eval/cvat_tools/folder_parser.py      |    4 +
 .../dataset_builders/cvat_dataset_builder.py  | 1398 ++---------------
 .../cvat_preannotation_builder.py             |   68 +-
 docling_eval/utils/json_dataset_joiner.py     |   40 +-
 pyproject.toml                                |    2 +-
 .../case_01_annotations.xml                   |  684 +-------
 .../case_02_annotations.xml                   |  465 +-----
 tests/test_cvat.py                            |   11 +-
 uv.lock                                       |  248 +--
 9 files changed, 428 insertions(+), 2492 deletions(-)

diff --git a/docling_eval/cvat_tools/folder_parser.py b/docling_eval/cvat_tools/folder_parser.py
index 74516e2..7eec317 100644
--- a/docling_eval/cvat_tools/folder_parser.py
+++ b/docling_eval/cvat_tools/folder_parser.py
@@ -85,9 +85,13 @@ def parse_pages_from_xml(
 
     for image_el in root.findall(".//image"):
         image_name = image_el.attrib["name"]
+
+        # Extract hash and page number from filename
+        # Expected format: doc_{hash}_page_{number}.{ext}
         doc_hash = extract_doc_hash_from_filename(image_name)
         page_number = extract_page_number_from_filename(image_name)
 
+        # Find the actual file path
         # Prefer cvat_tasks/ over page_imgs/ to eliminate redundancy
         # (images are identical, cvat_tasks/ is always created for CVAT upload)
         image_path: Optional[Path] = None
diff --git a/docling_eval/dataset_builders/cvat_dataset_builder.py b/docling_eval/dataset_builders/cvat_dataset_builder.py
index cc8fef1..22abf50 100644
--- a/docling_eval/dataset_builders/cvat_dataset_builder.py
+++ b/docling_eval/dataset_builders/cvat_dataset_builder.py
@@ -3,51 +3,27 @@
 import logging
 import os
 import sys
+import tempfile
 import zipfile
 from io import BytesIO
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Tuple, Union, cast
-
-import xmltodict  # type: ignore[import]
-from docling.backend.docling_parse_v4_backend import DoclingParseV4DocumentBackend
-from docling.datamodel.base_models import InputFormat
-from docling.datamodel.document import InputDocument
-from docling_core.types.doc import BoundingBox, CoordOrigin, Size
-from docling_core.types.doc.document import (
-    ContentLayer,
-    DoclingDocument,
-    FloatingItem,
-    GraphData,
-    GroupItem,
-    ImageRef,
-    ListItem,
-    NodeItem,
-    PageItem,
-    ProvenanceItem,
-    TableData,
-    TableItem,
-)
-from docling_core.types.doc.labels import DocItemLabel, GroupLabel
-from docling_core.types.doc.page import SegmentedPage, SegmentedPdfPage, TextCellUnit
+from typing import Dict, Iterable, List, Optional, Tuple, Union
+
+from docling_core.types.doc.document import DoclingDocument, TableData, TableItem
+from docling_core.types.doc.labels import DocItemLabel
 from docling_core.types.io import DocumentStream
-from PIL import Image
 from tqdm import tqdm
 
-from docling_eval.datamodels.cvat_types import (
-    AnnotatedImage,
-    AnnotationOverview,
-    BenchMarkDirs,
+from docling_eval.cvat_tools.cvat_to_docling import (
+    CVATConversionResult,
+    convert_cvat_folder_to_docling,
 )
+from docling_eval.cvat_tools.folder_parser import parse_cvat_folder
+from docling_eval.datamodels.cvat_types import AnnotationOverview, BenchMarkDirs
 from docling_eval.datamodels.dataset_record import DatasetRecord
 from docling_eval.datamodels.types import BenchMarkColumns, EvaluationModality
 from docling_eval.dataset_builders.dataset_builder import BaseEvaluationDatasetBuilder
-from docling_eval.utils.utils import (
-    crop_bounding_box,
-    extract_images,
-    from_pil_to_base64uri,
-    get_binary,
-    get_binhash,
-)
+from docling_eval.utils.utils import extract_images, get_binary, get_binhash
 
 # Configure logging
 _log = logging.getLogger(__name__)
@@ -78,7 +54,8 @@ class CvatDatasetBuilder(BaseEvaluationDatasetBuilder):
     """
     Dataset builder that creates a dataset from CVAT annotations.
 
-    This class processes CVAT annotations and creates a new ground truth dataset.
+    This class uses the modern `convert_cvat_folder_to_docling()` converter
+    to process CVAT annotations and creates a new ground truth dataset in parquet format.
     """
 
     def __init__(
@@ -93,12 +70,9 @@ def __init__(
 
         Args:
             name: Name of the dataset
-            cvat_source_dir: Directory containing CVAT annotations
+            dataset_source: Directory containing CVAT annotations
             target: Path where the new dataset will be saved
             split: Dataset split to use
-            begin_index: Start index for processing
-            end_index: End index for processing
-            do_visualization: Whether to generate HTML visualizations
         """
         super().__init__(
             name=name,
@@ -112,6 +86,7 @@ def __init__(
         self.benchmark_dirs.set_up_directory_structure(
             source=dataset_source, target=dataset_source
         )
+        self._temp_json_dir: Optional[Path] = None
 
     def _resolve_dataset_path(self, path: Union[Path, str]) -> Path:
         """Resolve paths relative to the dataset root when necessary."""
@@ -214,1192 +189,154 @@ def save_to_disk(
             chunk_size, max_num_chunks, do_visualization=do_visualization
         )
 
-    def find_box(
-        self, boxes: List[Dict], point: Tuple[float, float]
-    ) -> Tuple[int, Dict]:
-        """
-        Find the box containing a point.
-
-        Args:
-            boxes: List of boxes
-            point: Point coordinates (x, y)
-
-        Returns:
-            Tuple of (box_index, box)
-        """
-        index = -1
-        area = 1e9
-        box_result = {}
-
-        for i, box in enumerate(boxes):
-            # Ensure box coordinates are valid
-            if not (box["l"] < box["r"] and box["t"] < box["b"]):
-                continue
-
-            if box["l"] <= point[0] <= box["r"] and box["t"] <= point[1] <= box["b"]:
-                current_area = (box["r"] - box["l"]) * (box["b"] - box["t"])
-                if index == -1 or current_area < area:
-                    area = current_area
-                    index = i
-                    box_result = box
-
-        if index == -1:
-            _log.error(f"Point {point} is not in any bounding-box!")
-            return -1, {}
-
-        return index, box_result
-
-    def parse_annotation(self, image_annot: Dict) -> Tuple:
-        """
-        Parse a CVAT annotation for an image.
-
-        Args:
-            image_annot: Annotation data for an image
-
-        Returns:
-            Tuple of parsed annotation data
-        """
-        basename: str = image_annot["@name"]
-        keep: bool = False
-
-        boxes: List[Dict] = []
-        lines: List[Dict] = []
-
-        reading_order: Dict = {}
-
-        to_captions: List[Dict] = []
-        to_footnotes: List[Dict] = []
-        to_values: List[Dict] = []
-
-        merges: List[Dict] = []
-        group: List[Dict] = []
-
-        if "box" not in image_annot or "polyline" not in image_annot:
-            _log.warning(
-                f"Skipping {basename} because no `box` nor `polyline` is found"
-            )
-            return (
-                basename,
-                keep,
-                boxes,
-                lines,
-                reading_order,
-                to_captions,
-                to_footnotes,
-                to_values,
-                merges,
-                group,
-            )
-
-        # Process boxes
-        if isinstance(image_annot["box"], dict):
-            boxes = [image_annot["box"]]
-        elif isinstance(image_annot["box"], list):
-            boxes = image_annot["box"]
-        else:
-            _log.error(f"Could not get boxes for {basename}")
-            return (
-                basename,
-                keep,
-                boxes,
-                lines,
-                reading_order,
-                to_captions,
-                to_footnotes,
-                to_values,
-                merges,
-                group,
-            )
-
-        # Process polylines
-        if isinstance(image_annot["polyline"], dict):
-            lines = [image_annot["polyline"]]
-        elif isinstance(image_annot["polyline"], list):
-            lines = image_annot["polyline"]
-        else:
-            _log.error(f"Could not get lines for {basename}")
-            return (
-                basename,
-                keep,
-                boxes,
-                lines,
-                reading_order,
-                to_captions,
-                to_footnotes,
-                to_values,
-                merges,
-                group,
-            )
-
-        # Convert box coordinates to floats
-        for i, box in enumerate(boxes):
-            boxes[i]["b"] = float(box["@ybr"])
-            boxes[i]["t"] = float(box["@ytl"])
-            boxes[i]["l"] = float(box["@xtl"])
-            boxes[i]["r"] = float(box["@xbr"])
-
-        # Process polyline points and find corresponding boxes
-        for i, line in enumerate(lines):
-            points = []
-            for point_str in line["@points"].split(";"):
-                coords = point_str.split(",")
-                if len(coords) == 2:
-                    points.append((float(coords[0]), float(coords[1])))
-
-            boxids = []
-            for point in points:
-                bind, _ = self.find_box(boxes=boxes, point=point)
-                if 0 <= bind < len(boxes):
-                    boxids.append(bind)
-                else:
-                    boxids.append(-1)
-
-            lines[i]["points"] = points
-            lines[i]["boxids"] = boxids
-
-        # Categorize lines by label
-        for line in lines:
-            label = line["@label"]
-            if label == "reading_order":
-                # There should be only one reading order
-                if reading_order:
-                    _log.warning(
-                        f"Multiple reading orders found in {basename}, using the last one"
-                    )
-                keep = True
-                reading_order = line
-            elif label == "to_caption":
-                to_captions.append(line)
-            elif label == "to_footnote":
-                to_footnotes.append(line)
-            elif label == "to_value":
-                to_values.append(line)
-            elif label in ["next_text", "merge"]:
-                merges.append(line)
-            elif label in ["next_figure", "group"]:
-                group.append(line)
-            else:
-                _log.error(f"Ignoring lines with label {label}")
-
-        return (
-            basename,
-            keep,
-            boxes,
-            lines,
-            reading_order,
-            to_captions,
-            to_footnotes,
-            to_values,
-            merges,
-            group,
-        )
-
-    def create_prov(
-        self,
-        box: Dict,
-        page_no: int,
-        page_bbox: BoundingBox,
-        # img_width: float,
-        # img_height: float,
-        pdf_width: float,
-        pdf_height: float,
-        origin: CoordOrigin = CoordOrigin.TOPLEFT,
-    ) -> Tuple[ProvenanceItem, BoundingBox]:
-        """
-        Create provenance item and bounding box from a box.
-
-        Args:
-            box: Box data
-            page_no: Page number
-            img_width: Image width
-            img_height: Image height
-            pdf_width: PDF width
-            pdf_height: PDF height
-            origin: Coordinate origin
-
-        Returns:
-            Tuple of (provenance_item, bounding_box)
-        """
-        bbox = BoundingBox(
-            l=pdf_width * (box["l"] - page_bbox.l) / float(page_bbox.width),
-            r=pdf_width * (box["r"] - page_bbox.l) / float(page_bbox.width),
-            b=pdf_height * box["b"] / float(page_bbox.height),
-            t=pdf_height * box["t"] / float(page_bbox.height),
-            coord_origin=origin,
-        )
-        prov = ProvenanceItem(page_no=page_no, bbox=bbox, charspan=(0, 0))
-        return prov, bbox
-
-    def get_page_no_and_coord_origin(
-        self, box: Dict, desc: AnnotatedImage, doc: DoclingDocument
-    ) -> tuple[int, BoundingBox, ImageRef]:
-        """
-        Get page-number and coord-origin for composed image.
-        """
-        page_no = -1
-        page_bbox = BoundingBox(l=0.0, r=0, t=0, b=0, coord_origin=CoordOrigin.TOPLEFT)
-
-        item_bbox = BoundingBox(
-            l=box["l"],
-            r=box["r"],
-            t=box["t"],
-            b=box["b"],
-            coord_origin=CoordOrigin.TOPLEFT,
-        )
-
-        for key, val in desc.page_to_bbox.items():
-            curr_page_bbox = val.to_top_left_origin(page_height=val.height)
-
-            if item_bbox.intersection_over_self(curr_page_bbox) > 0.99:
-                page_no = key
-                page_bbox = curr_page_bbox
-                # _log.info(f"item is on page {page_no}")
-
-        if page_no not in doc.pages:
-            raise ValueError(f"Page {page_no} not found in document")
-
-        if doc.pages[page_no].image is None:
-            raise ValueError(f"Page {page_no} has no image reference")
-
-        return page_no, page_bbox, cast(ImageRef, doc.pages[page_no].image)
-
-    def get_label_prov_and_text(
-        self,
-        box: Dict,
-        page_no: int,
-        page_bbox: BoundingBox,  # Bounding box inside the collated image (with multiple pages)
-        pdf_width: float,
-        pdf_height: float,
-        parsed_page: SegmentedPdfPage,
-    ) -> Tuple[DocItemLabel, ProvenanceItem, str]:
-        """
-        Get label, provenance, and text from a box.
-
-        Args:
-            box: Box data
-            page_no: Page number
-            img_width: Image width
-            img_height: Image height
-            pdf_width: PDF width
-            pdf_height: PDF height
-            parser: PDF parser
-            parsed_page: Parsed page data
-
-        Returns:
-            Tuple of (label, provenance, text)
-        """
-        assert page_no > 0
-
-        prov, bbox = self.create_prov(
-            box=box,
-            page_no=page_no,
-            page_bbox=page_bbox,
-            # img_width=img_width,
-            # img_height=img_height,
-            pdf_width=pdf_width,
-            pdf_height=pdf_height,
-        )
-
-        label = DocItemLabel(box["@label"])
-
-        # PDF coordinates have origin at bottom-left, convert accordingly
-        text_cells = parsed_page.get_cells_in_bbox(
-            cell_unit=TextCellUnit.LINE, bbox=prov.bbox
-        )
-        text = " ".join([t.text for t in text_cells])
-        text = text.replace("  ", " ")
-
-        prov.charspan = (0, len(text))
-
-        return label, prov, text
-
-    def get_page_imageref(self, page_no: int, doc: DoclingDocument) -> ImageRef:
-        """
-        Get the image reference for a page.
-
-        Args:
-            page_no: Page number
-            doc: Document
-
-        Returns:
-            ImageRef for the page
-        """
-        if page_no not in doc.pages:
-            raise ValueError(f"Page {page_no} not found in document")
-
-        if doc.pages[page_no].image is None:
-            raise ValueError(f"Page {page_no} has no image reference")
-
-        return cast(ImageRef, doc.pages[page_no].image)
-
-    def get_next_provs(
-        self,
-        boxid: int,
-        text: str,
-        boxes: List[Dict],
-        merges: List[Dict],
-        already_added: List[int],
-        desc: AnnotatedImage,
-        true_doc: DoclingDocument,
-        parsed_pages: dict[int, SegmentedPdfPage],
-    ) -> Tuple[List[ProvenanceItem], str, List[int]]:
-        """
-        Get next provenance items for merged text.
-
-        Args:
-            page_no: Page number
-            boxid: Box ID
-            text: Current text
-            boxes: List of boxes
-            merges: List of merge lines
-            already_added: List of already added box IDs
-            true_doc: Document
-            parser: PDF parser
-            parsed_page: Parsed page data
-
-        Returns:
-            Tuple of (next_provenance_items, updated_text, updated_already_added)
-        """
-        # true_page_imageref = self.get_page_imageref(page_no=page_no, doc=true_doc)
-
-        labels_ = []
-
-        next_provs = []
-        for merge in merges:
-            if len(merge["boxids"]) > 1 and merge["boxids"][0] == boxid:
-                for l in range(1, len(merge["boxids"])):
-                    boxid_ = merge["boxids"][l]
-                    already_added.append(boxid_)
-
-                    page_no_, page_bbox_, imgref_ = self.get_page_no_and_coord_origin(
-                        box=boxes[boxid_], desc=desc, doc=true_doc
-                    )
-
-                    label_, prov_, text_ = self.get_label_prov_and_text(
-                        box=boxes[boxid_],
-                        page_no=page_no_,
-                        page_bbox=page_bbox_,
-                        # img_width=true_page_imageref.size.width,
-                        # img_height=true_page_imageref.size.height,
-                        pdf_width=true_doc.pages[page_no_].size.width,
-                        pdf_height=true_doc.pages[page_no_].size.height,
-                        parsed_page=parsed_pages[page_no_],
-                    )
-
-                    prov_.charspan = (len(text) + 1, len(text) + 1 + len(text_))
-                    text = text + " " + text_
-                    next_provs.append(prov_)
-
-                    labels_.append(label_)
-
-        assert len(set(labels_)) <= 1, f"{len(set(labels_))}<=1 for {labels_}"
-
-        return next_provs, text, already_added
-
-    def get_grouped_images(
-        self,
-        boxid: int,
-        boxes: List[Dict],
-        group_lines: List[Dict],
-        already_added: List[int],
-        desc: AnnotatedImage,
-        true_doc: DoclingDocument,
-        parsed_pages: dict[int, SegmentedPdfPage],
-    ) -> tuple[FloatingItem, list[int]]:
-        boxids = [boxid]
-        for group_line in group_lines:
-            if len(group_line["boxids"]) > 1 and group_line["boxids"][0] == boxid:
-                for l in range(1, len(group_line["boxids"])):
-                    boxids.append(group_line["boxids"][l])
-                    already_added.append(boxids[-1])
-
-        # Add picture to document
-        picture_item = true_doc.add_picture(prov=None, image=None)
-
-        """
-        picture_group = true_doc.add_group(
-            name="image-group", label=GroupLabel.PICTURE_AREA, parent=picture_item
-        )
-        """
-
-        pagenos_to_bbox: dict[int, list[BoundingBox]] = {}
-        for boxid_ in boxids:
-            page_no_, page_bbox_, true_page_imgref_ = self.get_page_no_and_coord_origin(
-                box=boxes[boxid_], desc=desc, doc=true_doc
-            )
-
-            assert true_page_imgref_.pil_image is not None
-            true_page_pilimage_: Image.Image = true_page_imgref_.pil_image
-
-            label_, prov_, text_ = self.get_label_prov_and_text(
-                box=boxes[boxid_],
-                page_no=page_no_,
-                page_bbox=page_bbox_,
-                pdf_width=true_doc.pages[page_no_].size.width,
-                pdf_height=true_doc.pages[page_no_].size.height,
-                parsed_page=parsed_pages[page_no_],
-            )
-            picture_item.prov.append(prov_)
-
-            """
-            # Crop image from page based on bounding box
-            crop_image = crop_bounding_box(
-                page_image=true_page_pilimage_,
-                page=true_doc.pages[page_no_],
-                bbox=prov_.bbox,
-            )
-
-            # Create image reference
-            imgref = ImageRef(
-                mimetype="image/png",
-                dpi=72,
-                size=Size(width=crop_image.width, height=crop_image.height),
-                uri=from_pil_to_base64uri(crop_image),
-            )
-
-            if true_doc.pages[page_no_].image is not None:
-                imgref.dpi = true_doc.pages[page_no_].image.dpi  # type: ignore
-
-            # Add picture to document
-            picture_item_ = true_doc.add_picture(
-                prov=prov_, image=imgref, parent=picture_group
-            )
-
-            if prov_.page_no in pagenos_to_bbox:
-                pagenos_to_bbox[prov_.page_no].append(prov_.bbox)
-            else:
-                pagenos_to_bbox[prov_.page_no] = [prov_.bbox]
-            """
-
-        return picture_item, already_added
-
-    def add_captions_to_item(
-        self,
-        basename: str,
-        to_captions: List[Dict],
-        item: FloatingItem,
-        boxid: int,
-        boxes: List[Dict],
-        already_added: List[int],
-        desc: AnnotatedImage,
-        true_doc: DoclingDocument,
-        parsed_pages: dict[int, SegmentedPdfPage],
-    ) -> Tuple[DoclingDocument, List[int]]:
-        """
-        Add captions to a floating item.
-
-        Args:
-            basename: Base filename
-            to_captions: List of caption lines
-            item: Floating item
-            page_no: Page number
-            boxid: Box ID
-            boxes: List of boxes
-            already_added: List of already added box IDs
-            true_doc: Document
-            parser: PDF parser
-            parsed_page: Parsed page data
-
-        Returns:
-            Tuple of (updated_document, updated_already_added)
-        """
-        for to_caption in to_captions:
-            if to_caption["boxids"][0] == boxid:
-                for l in range(1, len(to_caption["boxids"])):
-                    boxid_ = to_caption["boxids"][l]
-                    already_added.append(boxid_)
-
-                    page_no_, page_bbox_, imgref_ = self.get_page_no_and_coord_origin(
-                        box=boxes[boxid_], desc=desc, doc=true_doc
-                    )
-
-                    label, prov, text = self.get_label_prov_and_text(
-                        box=boxes[boxid_],
-                        page_no=page_no_,
-                        page_bbox=page_bbox_,
-                        pdf_width=true_doc.pages[page_no_].size.width,
-                        pdf_height=true_doc.pages[page_no_].size.height,
-                        parsed_page=parsed_pages[page_no_],
-                    )
-
-                    caption_ref = true_doc.add_text(
-                        label=DocItemLabel.CAPTION, prov=prov, text=text
-                    )
-                    item.captions.append(caption_ref.get_ref())
-
-                    if label != DocItemLabel.CAPTION:
-                        _log.warning(f"{label} != DocItemLabel.CAPTION for {basename}")
-
-        return true_doc, already_added
-
-    def add_footnotes_to_item(
-        self,
-        basename: str,
-        to_footnotes: List[Dict],
-        item: FloatingItem,
-        boxid: int,
-        boxes: List[Dict],
-        already_added: List[int],
-        desc: AnnotatedImage,
-        true_doc: DoclingDocument,
-        parsed_pages: dict[int, SegmentedPdfPage],
-    ) -> Tuple[DoclingDocument, List[int]]:
-        """
-        Add footnotes to a floating item.
-
-        Args:
-            basename: Base filename
-            to_footnotes: List of footnote lines
-            item: Floating item
-            page_no: Page number
-            boxid: Box ID
-            boxes: List of boxes
-            already_added: List of already added box IDs
-            true_doc: Document
-            parser: PDF parser
-            parsed_page: Parsed page data
-
-        Returns:
-            Tuple of (updated_document, updated_already_added)
-        """
-        for to_footnote in to_footnotes:
-            if to_footnote["boxids"][0] == boxid:
-                for l in range(1, len(to_footnote["boxids"])):
-                    boxid_ = to_footnote["boxids"][l]
-                    already_added.append(boxid_)
-
-                    footnote_box = boxes[boxid_]
-
-                    page_no_, page_bbox_, imgref_ = self.get_page_no_and_coord_origin(
-                        box=boxes[boxid_], desc=desc, doc=true_doc
-                    )
-
-                    label, prov, text = self.get_label_prov_and_text(
-                        box=boxes[boxid_],
-                        page_no=page_no_,
-                        page_bbox=page_bbox_,
-                        pdf_width=true_doc.pages[page_no_].size.width,
-                        pdf_height=true_doc.pages[page_no_].size.height,
-                        parsed_page=parsed_pages[page_no_],
-                    )
-
-                    footnote_ref = true_doc.add_text(
-                        label=DocItemLabel.FOOTNOTE, prov=prov, text=text
-                    )
-                    item.footnotes.append(footnote_ref.get_ref())
-
-                    if label != DocItemLabel.FOOTNOTE:
-                        _log.warning(f"{label} != DocItemLabel.FOOTNOTE for {basename}")
-
-        return true_doc, already_added
-
-    def add_listitems_to_group(
-        self,
-        list_group: GroupItem,
-        boxid: int,
-        boxes: List[Dict],
-        already_added: List[int],
-        desc: AnnotatedImage,
-        true_doc: DoclingDocument,
-        parsed_pages: dict[int, SegmentedPdfPage],
-    ) -> Tuple[DoclingDocument, List[int], ListItem]:
-        """
-        Add listitem to list-group.
-        """
-
-        page_no_, page_bbox_, imgref_ = self.get_page_no_and_coord_origin(
-            box=boxes[boxid], desc=desc, doc=true_doc
-        )
-
-        label, prov, text = self.get_label_prov_and_text(
-            box=boxes[boxid],
-            page_no=page_no_,
-            page_bbox=page_bbox_,
-            pdf_width=true_doc.pages[page_no_].size.width,
-            pdf_height=true_doc.pages[page_no_].size.height,
-            parsed_page=parsed_pages[page_no_],
-        )
-
-        list_item = true_doc.add_list_item(prov=prov, text=text, parent=list_group)
-        already_added.append(boxid)
-
-        return true_doc, already_added, list_item
-
-    def is_first_of_merge(self, boxid: int, merges: list[dict]) -> tuple[bool, list]:
-        """Is first of merge."""
-
-        for _ in merges:
-            if boxid == _["boxids"][0]:
-                return True, _["boxids"][1:]
-
-        return False, []
-
-    def is_first_of_group(self, boxid: int, groups: list[dict]) -> tuple[bool, list]:
-        """Is first of group."""
-
-        for _ in groups:
-            if boxid == _["boxids"][0]:
-                return True, _["boxids"][1:]
-
-        return False, []
-
-    def is_linked(
-        self,
-        boxid: int,
-        links: list[dict],
-        groups: list[dict] = [],
-        merges: list[dict] = [],
-    ) -> bool:
-        """Check if boxid is in a link."""
-
-        # Find all boxids that are linked to the original boxid
-        # using the groups and merges
-        boxids = [boxid]
-        for _ in merges:
-            if boxid in _["boxids"]:
-                boxids.extend(_["boxids"])
-
-        for _ in groups:
-            if boxid in _["boxids"]:
-                boxids.extend(_["boxids"])
-
-        # If any of the associated boxids is linked, then
-        # consider it linked
-        boxids = list(set(boxids))
-        for link in links:
-            for bid in boxids:
-                if bid in link["boxids"]:
-                    return True
-
-        return False
-
-    def create_true_document(
-        self, basename: str, annot: Dict, desc: AnnotatedImage
-    ) -> Optional[DoclingDocument]:
-        """
-        Create a ground truth document from CVAT annotations.
-
-        Args:
-            basename: Base filename
-            annot: Annotation data
-            desc: Annotated image description
-
-        Returns:
-            DoclingDocument or None if creation fails
-        """
-        # Parse the annotation
-        (
-            given_basename,
-            keep,
-            boxes,
-            lines,
-            reading_order,
-            to_captions,
-            to_footnotes,
-            to_values,
-            merges,
-            group,
-        ) = self.parse_annotation(annot)
-
-        assert given_basename == basename
-
-        if not keep:
-            _log.error(f"Incorrect annotation for {basename}: no reading order found")
-            return None
-
-        # Original Groundtruth and Prediction files
-        orig_file = self._resolve_dataset_path(desc.document_file)
-
-        if not orig_file.exists():
-            _log.error(f"Missing original files for {basename}: {orig_file}")
-            exit(-1)
-            return None
-
-        orig_doc = DoclingDocument.load_from_json(filename=orig_file)
-
-        # Original PDF file
-        pdf_file = self._resolve_dataset_path(desc.bin_file)
-        if not pdf_file.exists():
-            _log.error(f"Missing PDF file for {basename}: {pdf_file}")
-            return None
-
-        in_doc = InputDocument(
-            path_or_stream=pdf_file,
-            format=InputFormat.PDF,
-            backend=DoclingParseV4DocumentBackend,
-        )
-
-        doc_backend: DoclingParseV4DocumentBackend = in_doc._backend  # type: ignore
-
-        # Parse each page
-        parsed_pages: Dict[int, SegmentedPdfPage] = {}
-        for i, page_no in enumerate(desc.page_nos):
-            seg_page = doc_backend.load_page(page_no - 1).get_segmented_page()
-            assert seg_page is not None
-            parsed_pages[page_no] = seg_page
-
-        doc_backend.unload()
-
-        # Create Ground Truth document
-        new_doc = DoclingDocument(name=f"{basename}")
-
-        # Copy page images from predicted document
-        for i, page_no in enumerate(desc.page_nos):
-            # PDF dimensions
-            pdf_width = parsed_pages[page_no].dimension.width
-            pdf_height = parsed_pages[page_no].dimension.height
-
-            # Image file
-            img_file = self._resolve_dataset_path(desc.page_img_files[i])
-            page_image = Image.open(str(img_file))
-            img_width = page_image.width
-            img_height = page_image.height
-
-            # Check if predicted document has the page
-            if page_no not in orig_doc.pages or orig_doc.pages[page_no] is None:
-                _log.error(f"Missing page {page_no} in predicted document, skipping...")
-                continue
-
-            orig_page_item = orig_doc.pages[page_no]
-            orig_page_image_ref = orig_page_item.image
-
-            if orig_page_image_ref is None:
-                _log.error(f"Missing image reference for page {page_no}, skipping...")
-                continue
-
-            # Create image reference and page item
-            image_ref = ImageRef(
-                mimetype="image/png",
-                dpi=orig_page_image_ref.dpi,
-                size=Size(width=float(img_width), height=float(img_height)),
-                uri=from_pil_to_base64uri(page_image),
+    def _detect_xml_pattern(self) -> str:
+        """Detect the XML pattern from available XML files in cvat_tasks directory."""
+        assert isinstance(self.dataset_source, Path), "dataset_source must be a Path"
+        tasks_dir = self.dataset_source / "cvat_tasks"
+        if tasks_dir.exists():
+            xml_files = sorted(tasks_dir.glob("task_*_set_*.xml"))
+            if xml_files:
+                # Check if we have set_A files
+                set_a_files = [f for f in xml_files if "_set_A" in f.name]
+                if set_a_files:
+                    return "task_{xx}_set_A"
+                # Extract pattern from first file
+                import re
+
+                first_file = xml_files[0].name
+                match = re.match(r"task_(\d+)_set_([A-Z])\.xml", first_file)
+                if match:
+                    return f"task_{{xx}}_set_{match.group(2)}"
+
+        # Default pattern
+        return "task_{xx}_set_A"
+
+    def _create_dataset_record_from_json(
+        self, json_path: Path, doc_hash: str, folder_structure
+    ) -> Optional[DatasetRecord]:
+        """Create a DatasetRecord from a JSON file and corresponding PDF."""
+        try:
+            # Load DoclingDocument from JSON
+            doc = DoclingDocument.load_from_json(json_path)
+
+            # Find PDF file from folder structure
+            if doc_hash not in folder_structure.documents:
+                _log.warning(f"Document hash {doc_hash} not found in folder structure")
+                return None
+
+            cvat_doc = folder_structure.documents[doc_hash]
+            pdf_path = cvat_doc.bin_file
+
+            if not pdf_path.exists():
+                _log.warning(f"PDF file {pdf_path} not found for {doc_hash}")
+                return None
+
+            # Extract images from document
+            doc, pictures, page_images = extract_images(
+                document=doc,
+                pictures_column=BenchMarkColumns.GROUNDTRUTH_PICTURES.value,
+                page_images_column=BenchMarkColumns.GROUNDTRUTH_PAGE_IMAGES.value,
             )
 
-            page_item = PageItem(
-                page_no=page_no,
-                size=Size(width=float(pdf_width), height=float(pdf_height)),
-                image=image_ref,
+            # Load PDF as binary data
+            pdf_bytes = get_binary(pdf_path)
+            pdf_stream = DocumentStream(name=pdf_path.name, stream=BytesIO(pdf_bytes))
+
+            # Create dataset record
+            record = DatasetRecord(
+                doc_id=doc.name or json_path.stem,
+                doc_path=str(json_path.stem),
+                doc_hash=get_binhash(pdf_bytes),
+                ground_truth_doc=doc,
+                ground_truth_pictures=pictures,
+                ground_truth_page_images=page_images,
+                original=pdf_stream,
+                mime_type=cvat_doc.mime_type or "application/pdf",
+                modalities=[
+                    EvaluationModality.LAYOUT,
+                    EvaluationModality.READING_ORDER,
+                    EvaluationModality.CAPTIONING,
+                ],
             )
 
-            new_doc.pages[page_no] = page_item
+            return record
 
-        # Process items based on reading order
-        already_added: List[int] = []
-
-        # Sanity check for reading order
-        if not reading_order or "boxids" not in reading_order:
-            _log.error(f"Invalid reading order for {basename}")
+        except Exception as e:
+            _log.error(f"Error creating dataset record from {json_path}: {e}")
             return None
 
-        boxids: list[int] = reading_order["boxids"]
-        boxid_to_nodeitem: dict[int, NodeItem] = {}
-
-        for ind, boxid in enumerate(boxids):
-            if boxid in already_added:
-                _log.debug(f"Box {boxid} is already added, skipping...")
-                continue
-
-            first_in_group, rest_in_group = self.is_first_of_group(
-                boxid=boxid, groups=group
-            )
-
-            # Get page-number, bbox of page in image and original page-image reference for the page
-            page_no, page_bbox, true_page_imageref = self.get_page_no_and_coord_origin(
-                box=boxes[boxid], desc=desc, doc=new_doc
-            )
-
-            if page_no not in new_doc.pages or new_doc.pages[page_no] is None:
-                _log.error(f"Page {page_no} not found in document, skipping...")
-                continue
-
-            assert true_page_imageref.pil_image is not None
-            true_page_pilimage: Image.Image = true_page_imageref.pil_image
-
-            # Get label, provenance, and text for the box
-            label, prov, text = self.get_label_prov_and_text(
-                box=boxes[boxid],
-                page_no=page_no,
-                page_bbox=page_bbox,
-                pdf_width=new_doc.pages[page_no].size.width,
-                pdf_height=new_doc.pages[page_no].size.height,
-                parsed_page=parsed_pages[page_no],
-            )
-
-            # Process merged text
-            next_provs, text, already_added = self.get_next_provs(
-                boxid=boxid,
-                text=text,
-                boxes=boxes,
-                merges=merges,
-                already_added=already_added,
-                desc=desc,
-                true_doc=new_doc,
-                parsed_pages=parsed_pages,
-            )
-
-            # Add item to document based on label
-            if label in [
-                DocItemLabel.TEXT,
-                DocItemLabel.PARAGRAPH,
-                DocItemLabel.REFERENCE,
-                DocItemLabel.TITLE,
-            ]:
-                current_item = new_doc.add_text(label=label, prov=prov, text=text)
-                for next_prov in next_provs:
-                    current_item.prov.append(next_prov)
-
-                boxid_to_nodeitem[boxid] = current_item
-
-            elif label in [DocItemLabel.PAGE_HEADER, DocItemLabel.PAGE_FOOTER]:
-                current_item = new_doc.add_text(
-                    label=label,
-                    prov=prov,
-                    text=text,
-                    content_layer=ContentLayer.FURNITURE,
-                )
-
-                boxid_to_nodeitem[boxid] = current_item
-
-            elif label == DocItemLabel.SECTION_HEADER:
-                level = 1
-                if (
-                    "attribute" in boxes[boxid]
-                    and "@name" in boxes[boxid]["attribute"]
-                    and boxes[boxid]["attribute"]["@name"] == "level"
-                    and "#text" in boxes[boxid]["attribute"]
-                ):
-                    level = int(boxes[boxid]["attribute"]["#text"])
-
-                current_item = new_doc.add_heading(prov=prov, text=text, level=level)
-                boxid_to_nodeitem[boxid] = current_item
-
-            elif label == DocItemLabel.CAPTION:
-                if not self.is_linked(
-                    boxid=boxid, links=to_captions, merges=merges, groups=group
-                ):
-                    _log.warning(f"Detected {label} that is not linked!")
-                    current_item = new_doc.add_text(label=label, prov=prov, text=text)
-                    boxid_to_nodeitem[boxid] = current_item
-
-                else:
-                    _log.debug(f"Ignoring {label}: assigned to other floating item")
-                    continue
-
-            elif label == DocItemLabel.FOOTNOTE:
-                if not self.is_linked(
-                    boxid=boxid, links=to_footnotes, merges=merges, groups=group
-                ):
-                    _log.warning(f"Detected {label} that is not linked!")
-                    current_item = new_doc.add_text(label=label, prov=prov, text=text)
-                    boxid_to_nodeitem[boxid] = current_item
-                else:
-                    _log.debug(f"Ignoring {label}: assigned to other floating item")
-                    continue
-
-            elif label in [
-                DocItemLabel.CHECKBOX_SELECTED,
-                DocItemLabel.CHECKBOX_UNSELECTED,
-            ]:
-                current_item = new_doc.add_text(label=label, prov=prov, text=text)
-                boxid_to_nodeitem[boxid] = current_item
-
-            elif label == DocItemLabel.LIST_ITEM:
-                if first_in_group:
-                    parent = None
-                    if (
-                        ind > 0
-                        and boxids[ind - 1] in boxid_to_nodeitem
-                        and isinstance(boxid_to_nodeitem[boxids[ind - 1]], ListItem)
-                    ):
-                        _log.debug("Found a list-item as a parent")
-                        parent = boxid_to_nodeitem[boxids[ind - 1]]
-
-                    list_group = new_doc.add_group(
-                        label=GroupLabel.ORDERED_LIST, parent=parent
-                    )
-
-                    current_item = new_doc.add_list_item(
-                        prov=prov, text=text, parent=list_group
-                    )
-                    boxid_to_nodeitem[boxid] = current_item
-
-                    for boxid_ in rest_in_group:
-                        new_doc, already_added, list_item = self.add_listitems_to_group(
-                            list_group=list_group,
-                            boxid=boxid_,
-                            boxes=boxes,
-                            already_added=already_added,
-                            desc=desc,
-                            true_doc=new_doc,
-                            parsed_pages=parsed_pages,
-                        )
-                        boxid_to_nodeitem[boxid_] = list_item
-                else:
-                    _log.warning("Found a list-item without a parent.")
-
-                    parent = None
-                    if (
-                        ind > 0
-                        and boxids[ind - 1] in boxid_to_nodeitem
-                        and isinstance(boxid_to_nodeitem[boxids[ind - 1]], ListItem)
-                    ):
-                        _log.debug("Found a list-item as a parent")
-                        parent = boxid_to_nodeitem[boxids[ind - 1]]
-
-                    # This could be commented out and is a case-by-case basis ...
-                    list_group = new_doc.add_group(
-                        label=GroupLabel.ORDERED_LIST, parent=parent
-                    )
-
-                    current_item = new_doc.add_list_item(
-                        prov=prov, text=text, parent=list_group
-                    )
-                    boxid_to_nodeitem[boxid] = current_item
-
-            elif label == DocItemLabel.FORMULA:
-                current_item = new_doc.add_text(label=label, prov=prov, text=text)
-                boxid_to_nodeitem[boxid] = current_item
-
-            elif label == DocItemLabel.CODE:
-                code_item = new_doc.add_code(text=text, prov=prov)
-                boxid_to_nodeitem[boxid] = code_item
-
-                new_doc, already_added = self.add_captions_to_item(
-                    basename=basename,
-                    to_captions=to_captions,
-                    item=code_item,
-                    boxid=boxid,
-                    boxes=boxes,
-                    already_added=already_added,
-                    desc=desc,
-                    true_doc=new_doc,
-                    parsed_pages=parsed_pages,
-                )
-
-                new_doc, already_added = self.add_footnotes_to_item(
-                    basename=basename,
-                    to_footnotes=to_footnotes,
-                    item=code_item,
-                    boxid=boxid,
-                    boxes=boxes,
-                    already_added=already_added,
-                    desc=desc,
-                    true_doc=new_doc,
-                    parsed_pages=parsed_pages,
-                )
-
-            elif label == DocItemLabel.FORM:
-                graph = GraphData(cells=[], links=[])
-                current_item = new_doc.add_form(graph=graph, prov=prov)
-                boxid_to_nodeitem[boxid] = current_item
-
-            elif label == DocItemLabel.KEY_VALUE_REGION:
-                graph = GraphData(cells=[], links=[])
-                current_item = new_doc.add_key_values(graph=graph, prov=prov)
-                boxid_to_nodeitem[boxid] = current_item
-
-            elif label in [DocItemLabel.TABLE, DocItemLabel.DOCUMENT_INDEX]:
-                # Try to find table data in original document
-                table_data = find_table_data(doc=orig_doc, prov=prov)
-
-                if table_data.num_rows <= 0 or table_data.num_cols <= 0:
-                    # Create an empty table structure if no data found
-                    table_data = TableData(num_rows=0, num_cols=0, table_cells=[])
-
-                table_item = new_doc.add_table(label=label, data=table_data, prov=prov)
-                boxid_to_nodeitem[boxid] = table_item
-
-                # Add captions and footnotes to table
-                new_doc, already_added = self.add_captions_to_item(
-                    basename=basename,
-                    to_captions=to_captions,
-                    item=table_item,
-                    boxid=boxid,
-                    boxes=boxes,
-                    already_added=already_added,
-                    desc=desc,
-                    true_doc=new_doc,
-                    parsed_pages=parsed_pages,
-                )
-
-                new_doc, already_added = self.add_footnotes_to_item(
-                    basename=basename,
-                    to_footnotes=to_footnotes,
-                    item=table_item,
-                    boxid=boxid,
-                    boxes=boxes,
-                    already_added=already_added,
-                    desc=desc,
-                    true_doc=new_doc,
-                    parsed_pages=parsed_pages,
-                )
-
-            elif label == DocItemLabel.PICTURE:
-                # Add picture(s) to document
-                picture_item, already_added = self.get_grouped_images(
-                    boxid=boxid,
-                    boxes=boxes,
-                    group_lines=group,
-                    already_added=already_added,
-                    desc=desc,
-                    true_doc=new_doc,
-                    parsed_pages=parsed_pages,
-                )
-                boxid_to_nodeitem[boxid] = picture_item
-
-                # Add captions and footnotes to picture
-                new_doc, already_added = self.add_captions_to_item(
-                    basename=basename,
-                    to_captions=to_captions,
-                    item=picture_item,
-                    boxid=boxid,
-                    boxes=boxes,
-                    already_added=already_added,
-                    desc=desc,
-                    true_doc=new_doc,
-                    parsed_pages=parsed_pages,
-                )
-
-                new_doc, already_added = self.add_footnotes_to_item(
-                    basename=basename,
-                    to_footnotes=to_footnotes,
-                    item=picture_item,
-                    boxid=boxid,
-                    boxes=boxes,
-                    already_added=already_added,
-                    desc=desc,
-                    true_doc=new_doc,
-                    parsed_pages=parsed_pages,
-                )
-            else:
-                _log.error(f"Ignoring annotated label: {label}")
-
-        return new_doc
-
-    def contains_reading_order(self, image_annot: Dict) -> bool:
-        """
-        Check if an image annotation contains reading order.
-
-        Args:
-            image_annot: Image annotation data
-
-        Returns:
-            True if the annotation contains reading order, False otherwise
-        """
-        if "box" not in image_annot:
-            return False
-
-        if "polyline" not in image_annot:
-            return False
-
-        if isinstance(image_annot["polyline"], dict):
-            lines = [image_annot["polyline"]]
-        elif isinstance(image_annot["polyline"], list):
-            lines = image_annot["polyline"]
-        else:
-            return False
-
-        cnt = 0
-        for line in lines:
-            if line["@label"] == "reading_order":
-                cnt += 1
-
-        return cnt == 1
-
-    def from_cvat_to_docling_document(
-        self, annotation_filenames: List[Path], overview: AnnotationOverview
-    ) -> Iterable[Tuple[str, AnnotatedImage, Optional[DoclingDocument]]]:
-        """
-        Convert CVAT annotations to DoclingDocument objects.
-
-        Args:
-            annotation_filenames: List of annotation filenames
-            overview: Annotation overview
-
-        Yields:
-            Tuple of (basename, annotated_image, docling_document)
-        """
-        for annot_file in annotation_filenames:
-            with open(str(annot_file), "r") as fr:
-                xml_data = fr.read()
-
-            # Convert XML to a Python dictionary
-            annot_data = xmltodict.parse(xml_data)
-
-            for image_annot in annot_data["annotations"]["image"]:
-                basename = image_annot["@name"]
-                _log.debug(f"basename: {basename}")
-
-                if basename not in overview.img_annotations:
-                    _log.warning(f"Skipping {basename}: not in overview file")
-                    continue
-
-                if not self.contains_reading_order(image_annot):
-                    _log.warning(f"Skipping {basename}: no reading-order detected")
-                    yield basename, overview.img_annotations[basename], None
-                else:
-                    try:
-                        true_doc = self.create_true_document(
-                            basename=basename,
-                            annot=image_annot,
-                            desc=overview.img_annotations[basename],
-                        )
-                    except Exception as e:
-                        _log.fatal("Exception occured", e)
-                    yield basename, overview.img_annotations[basename], true_doc
-
     def iterate(self) -> Iterable[DatasetRecord]:
         """
-        Create dataset records from CVAT annotations.
+        Create dataset records from CVAT annotations using modern converter.
 
-        This method processes all annotation files and creates dataset records
-        from valid annotations with reading order information.
+        This method uses `convert_cvat_folder_to_docling()` to convert CVAT annotations
+        to DoclingDocument JSON files, then converts those to DatasetRecord objects.
 
         Returns:
-            List of DatasetRecord objects
+            Iterable of DatasetRecord objects
         """
+        assert isinstance(self.dataset_source, Path), "dataset_source must be a Path"
+        # Detect XML pattern
+        xml_pattern = self._detect_xml_pattern()
+        _log.info(f"Using XML pattern: {xml_pattern}")
 
-        # Load the overview file
+        # Parse folder structure
         try:
-            overview = AnnotationOverview.load_from_json(
-                self.benchmark_dirs.overview_file
-            )
+            folder_structure = parse_cvat_folder(self.dataset_source, xml_pattern)
             _log.info(
-                f"loaded annotation overview file: {self.benchmark_dirs.overview_file}"
+                f"Found {len(folder_structure.documents)} documents in folder structure"
             )
-        except (FileNotFoundError, json.JSONDecodeError) as e:
-            _log.error(f"Failed to load annotation overview file: {e}")
+        except Exception as e:
+            _log.error(f"Failed to parse CVAT folder: {e}")
             raise
 
-        # Get annotation files
-        annot_files = self.get_annotation_files()
-        if not annot_files:
-            _log.error("No annotation files found in CVAT dir")
-            raise
-        else:
-            _log.info(f"loaded annotation files: {len(annot_files)}")
+        # Create temporary directory for JSON files
+        self._temp_json_dir = Path(tempfile.mkdtemp(prefix="cvat_dataset_builder_"))
+        _log.info(f"Using temporary JSON directory: {self._temp_json_dir}")
+
+        try:
+            # Step 1: Convert CVAT folder to DoclingDocument JSON files
+            _log.info("Converting CVAT annotations to DoclingDocument JSON files...")
+            conversion_results = convert_cvat_folder_to_docling(
+                folder_path=self.dataset_source,
+                xml_pattern=xml_pattern,
+                output_dir=self._temp_json_dir,
+                save_formats=["json"],
+                folder_structure=folder_structure,
+                log_validation=False,
+                force_ocr=False,
+                cvat_input_scale=2.0,
+                storage_scale=2.0,
+            )
 
-        # Calculate total items for effective indices
-        total_items = len(overview.img_annotations)
-        begin, end = self.get_effective_indices(total_items)
+            # Step 2: Convert JSON results to DatasetRecord objects
+            # Sort by document name for consistent ordering
+            sorted_results = sorted(
+                conversion_results.items(),
+                key=lambda x: folder_structure.documents[x[0]].doc_name,
+            )
 
-        # Log statistics
-        self.log_dataset_stats(total_items, end - begin)
+            # Calculate total items for effective indices
+            total_items = len(sorted_results)
+            begin, end = self.get_effective_indices(total_items)
 
-        item_count = 0
+            # Log statistics
+            self.log_dataset_stats(total_items, end - begin)
 
-        _log.info(f"Processing annotations from index {begin} to {end}")
+            _log.info(f"Processing documents from index {begin} to {end}")
 
-        try:
-            for basename, desc, true_doc in tqdm(
-                self.from_cvat_to_docling_document(annot_files, overview),
-                total=len(overview.img_annotations),
+            item_count = 0
+            for doc_hash, result in tqdm(
+                sorted_results,
+                total=len(sorted_results),
                 ncols=128,
-                desc="Creating documents from annotations",
+                desc="Creating dataset records",
             ):
-                # Skip if no document was created
-                if true_doc is None:
-                    _log.warning(f"No document created for {basename}, skipping")
+                # Skip failed conversions
+                if result.error is not None:
+                    _log.warning(
+                        f"Skipping {doc_hash} due to conversion error: {result.error}"
+                    )
                     continue
 
                 # Apply index filtering
@@ -1407,51 +344,36 @@ def iterate(self) -> Iterable[DatasetRecord]:
                 if item_count < begin:
                     continue
                 if item_count >= end:
-                    return
-
-                # Save the document as JSON
-                json_path = self.benchmark_dirs.json_anno_dir / f"{basename}.json"
-                true_doc.save_as_json(json_path)
+                    break
 
-                # Extract images
-                true_doc, true_pictures, true_page_images = extract_images(
-                    document=true_doc,
-                    pictures_column=BenchMarkColumns.GROUNDTRUTH_PICTURES.value,
-                    page_images_column=BenchMarkColumns.GROUNDTRUTH_PAGE_IMAGES.value,
-                )
-                _log.info(f"page-numbers: {true_doc.pages.keys()}")
+                # Find JSON file path
+                cvat_doc = folder_structure.documents[doc_hash]
+                json_path = self._temp_json_dir / f"{cvat_doc.doc_name}.json"
 
-                # Get PDF as binary data
-                pdf_file = self._resolve_dataset_path(desc.bin_file)
-                if not pdf_file.exists():
-                    _log.warning(f"PDF file {pdf_file} not found, skipping")
+                if not json_path.exists():
+                    _log.warning(
+                        f"JSON file not found: {json_path} (conversion may have failed silently)"
+                    )
                     continue
 
-                pdf_bytes = get_binary(pdf_file)
-                pdf_stream = DocumentStream(
-                    name=pdf_file.name, stream=BytesIO(pdf_bytes)
+                # Create DatasetRecord from JSON
+                record = self._create_dataset_record_from_json(
+                    json_path, doc_hash, folder_structure
                 )
 
-                # Create dataset record
-                record = DatasetRecord(
-                    doc_id=str(basename),
-                    doc_path=str(basename),
-                    doc_hash=get_binhash(pdf_bytes),
-                    ground_truth_doc=true_doc,
-                    ground_truth_pictures=true_pictures,
-                    ground_truth_page_images=true_page_images,
-                    original=pdf_stream,
-                    mime_type="application/pdf",
-                    modalities=[
-                        EvaluationModality.LAYOUT,
-                        EvaluationModality.READING_ORDER,
-                        EvaluationModality.CAPTIONING,
-                    ],
-                )
+                if record is not None:
+                    yield record
 
-                yield record
-        except Exception as e:
-            _log.error(f"Error processing annotations: {str(e)}")
+        finally:
+            # Clean up temporary directory
+            if self._temp_json_dir and self._temp_json_dir.exists():
+                import shutil
+
+                try:
+                    shutil.rmtree(self._temp_json_dir)
+                    _log.debug(f"Cleaned up temporary directory: {self._temp_json_dir}")
+                except Exception as e:
+                    _log.warning(f"Failed to clean up temporary directory: {e}")
 
 
 def find_table_data(doc: DoclingDocument, prov, iou_cutoff: float = 0.90):
diff --git a/docling_eval/dataset_builders/cvat_preannotation_builder.py b/docling_eval/dataset_builders/cvat_preannotation_builder.py
index 8fb9643..bd9ce86 100644
--- a/docling_eval/dataset_builders/cvat_preannotation_builder.py
+++ b/docling_eval/dataset_builders/cvat_preannotation_builder.py
@@ -77,7 +77,17 @@ def __init__(
         )
 
     def _relative_to_target(self, path: Union[Path, str]) -> Path:
-        """Return a path relative to the target directory when possible."""
+        """
+        Return a path relative to the target directory.
+
+        This ensures paths stored in cvat_overview.json are always relative,
+        preventing issues when the dataset is moved or used on different machines.
+
+        Handles both:
+        - Absolute paths under current target: uses relative_to()
+        - Absolute paths from moved datasets: finds target directory in path parents and uses relative_to()
+        - Relative paths: returns as-is (or extracts if starts with target name)
+        """
 
         if path is None:
             return Path("")
@@ -86,19 +96,45 @@ def _relative_to_target(self, path: Union[Path, str]) -> Path:
         if not str(path_obj):
             return path_obj
 
-        base = self.benchmark_dirs.target_dir
+        base = self.benchmark_dirs.target_dir.resolve()
+        base_name = base.name
 
+        # For absolute paths, try relative_to() first (normal case)
         if path_obj.is_absolute():
+            path_obj = path_obj.resolve()
             try:
                 return path_obj.relative_to(base)
             except ValueError:
-                base_name = base.name
-                if base_name in path_obj.parts:
-                    idx = path_obj.parts.index(base_name)
-                    rel_parts = path_obj.parts[idx + 1 :]
-                    if rel_parts:
-                        return Path(*rel_parts)
-                    return Path("")
+                # Path is not under current base - dataset may have been moved
+                # Use Pathlib to find target directory in path's parent chain
+                for parent in path_obj.parents:
+                    if parent.name == base_name:
+                        # Found target directory in parent chain, extract relative portion
+                        try:
+                            return path_obj.relative_to(parent)
+                        except ValueError:
+                            # Shouldn't happen, but handle gracefully
+                            continue
+                # If we can't find target directory in parents, return as-is
+                # (will be handled by _absolute_from_target when loading)
+                _log.debug(
+                    f"Path {path_obj} is not under target directory {base} "
+                    f"and target name '{base_name}' not found in parent chain. Keeping as absolute path."
+                )
+                return path_obj
+
+        # For non-absolute paths, check if they contain target directory name
+        # (handles cases where path was stored with target name in middle, e.g., moved datasets)
+        path_parts = path_obj.parts
+        if path_parts and base_name in path_parts:
+            # Find target directory name in path and extract relative portion after it
+            idx = path_parts.index(base_name)
+            rel_parts = path_parts[idx + 1 :]
+            if rel_parts:
+                return Path(*rel_parts)
+            return Path("")
+
+        # Path is already relative (doesn't contain target directory name)
         return path_obj
 
     def _store_image_annotation(
@@ -122,7 +158,13 @@ def _store_image_annotation(
         self.overview.img_annotations[filename] = annotated_image
 
     def _absolute_from_target(self, path: Union[Path, str]) -> Path:
-        """Return an absolute path rooted at the target directory when needed."""
+        """
+        Return an absolute path rooted at the target directory when needed.
+
+        This method handles both:
+        - Relative paths (stored in new cvat_overview.json files): prepends target_dir
+        - Absolute paths (from old cvat_overview.json files): returns as-is for backward compatibility
+        """
 
         if path is None:
             return Path("")
@@ -131,10 +173,14 @@ def _absolute_from_target(self, path: Union[Path, str]) -> Path:
         if not str(path_obj):
             return path_obj
 
+        # If path is already absolute (from old overview files), return as-is
+        # This maintains backward compatibility with existing datasets
         if path_obj.is_absolute():
             return path_obj
 
-        return self.benchmark_dirs.target_dir / path_obj
+        # For relative paths, prepend target directory (ensure target_dir is resolved to absolute)
+        target_dir = self.benchmark_dirs.target_dir.resolve()
+        return target_dir / path_obj
 
     def _export_from_dataset(self) -> AnnotationOverview:
         """
diff --git a/docling_eval/utils/json_dataset_joiner.py b/docling_eval/utils/json_dataset_joiner.py
index fc921ed..9a2a9ae 100644
--- a/docling_eval/utils/json_dataset_joiner.py
+++ b/docling_eval/utils/json_dataset_joiner.py
@@ -28,7 +28,7 @@
 
 def _load_prediction_json(
     prediction_record: DatasetRecord,
-) -> tuple[DoclingDocument, list, list, Optional[str]]:
+) -> tuple[DoclingDocument, list, list]:
     document = prediction_record.ground_truth_doc.model_copy(deep=True)
     prediction_doc, pictures, page_images = extract_images(
         document=document,
@@ -36,30 +36,7 @@ def _load_prediction_json(
         page_images_column=BenchMarkColumns.PREDICTION_PAGE_IMAGES.value,
     )
 
-    prediction_text: Optional[str] = None
-    original = prediction_record.original
-    if original is not None:
-        try:
-            raw_bytes: bytes
-            if isinstance(original, DocumentStream):
-                stream = original.stream
-                stream.seek(0)
-                raw_bytes = stream.read()
-                stream.seek(0)
-            elif isinstance(original, Path):
-                raw_bytes = original.read_bytes()
-            else:  # pragma: no cover
-                raise TypeError(f"Unsupported prediction source: {type(original)!r}")
-
-            prediction_text = raw_bytes.decode("utf-8")
-        except (OSError, UnicodeDecodeError) as exc:
-            _LOGGER.debug(
-                "Failed to decode prediction JSON for %s: %s",
-                prediction_record.doc_id,
-                exc,
-            )
-
-    return prediction_doc, pictures, page_images, prediction_text
+    return prediction_doc, pictures, page_images
 
 
 def _build_prediction_record(
@@ -70,10 +47,11 @@ def _build_prediction_record(
     *,
     prediction_format: PredictionFormats,
     predictor_info: Dict,
-    original_prediction: Optional[str],
 ) -> DatasetRecordWithPrediction:
     record_data = gt_record.model_dump()
-    record_data["original"] = gt_record.original
+    # Set 'original' to None - it's redundant since we have ground_truth_doc and images extracted
+    # The original JSON file with base64 images is not needed when we have the parsed document
+    record_data["original"] = None
     record_data["ground_truth_doc"] = gt_record.ground_truth_doc
     record_data["ground_truth_pictures"] = gt_record.ground_truth_pictures
     record_data["ground_truth_page_images"] = gt_record.ground_truth_page_images
@@ -86,7 +64,8 @@ def _build_prediction_record(
             "prediction_format": prediction_format,
             "predictor_info": predictor_info,
             "prediction_timings": None,
-            "original_prediction": original_prediction,
+            # Don't store original_prediction - it's redundant since we have predicted_doc and images extracted
+            "original_prediction": None,
             "status": ConversionStatus.SUCCESS,
         }
     )
@@ -164,8 +143,8 @@ def _generate_records() -> Iterator[DatasetRecordWithPrediction]:
                     continue
                 raise ValueError(message)
 
-            prediction_doc, pictures, page_images, original_prediction = (
-                _load_prediction_json(prediction_record)
+            prediction_doc, pictures, page_images = _load_prediction_json(
+                prediction_record
             )
 
             joined = _build_prediction_record(
@@ -175,7 +154,6 @@ def _generate_records() -> Iterator[DatasetRecordWithPrediction]:
                 page_images,
                 prediction_format=prediction_format,
                 predictor_info=predictor_info,
-                original_prediction=original_prediction,
             )
 
             if do_visualization and visualizations_dir is not None:
diff --git a/pyproject.toml b/pyproject.toml
index 45d0227..e815443 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,7 +25,7 @@ classifiers = [
 ]
 requires-python = '>=3.10,<4.0'
 dependencies = [
-    'docling[vlm] (>=2.56.1,<3.0.0)',
+    "docling[vlm]>=2.56.1,<3.0.0",
     "docling-core>=2.48.0,<3.0.0",
     'pydantic (>=2.0.0,<3.0.0)',
     'lxml (>=5.3.0,<6.0.0)',
diff --git a/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml b/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml
index 7771e7d..436ea3f 100644
--- a/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml
+++ b/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml
@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?>
+<?xml version='1.0' encoding='utf-8'?>
 <annotations>
   <version>1.1</version>
   <meta>
@@ -7,13 +7,13 @@
       <size>10</size>
       <mode>annotation</mode>
       <overlap>0</overlap>
-      <bugtracker></bugtracker>
+      <bugtracker />
       <created>2025-05-01 04:58:09.899418+00:00</created>
       <updated>2025-05-06 11:38:13.210060+00:00</updated>
       <subset>default</subset>
       <start_frame>0</start_frame>
       <stop_frame>9</stop_frame>
-      <frame_filter></frame_filter>
+      <frame_filter />
       <segments>
         <segment>
           <id>11</id>
@@ -26,7 +26,7 @@
         <username>Peter</username>
         <email>taa@zurich.ibm.com</email>
       </owner>
-      <assignee></assignee>
+      <assignee />
       <labels>
         <label>
           <name>caption</name>
@@ -58,8 +58,8 @@
               <name>latex</name>
               <mutable>True</mutable>
               <input_type>text</input_type>
-              <default_value></default_value>
-              <values></values>
+              <default_value />
+              <values />
             </attribute>
           </attributes>
         </label>
@@ -102,8 +102,8 @@
               <name>json</name>
               <mutable>True</mutable>
               <input_type>text</input_type>
-              <default_value></default_value>
-              <values></values>
+              <default_value />
+              <values />
             </attribute>
           </attributes>
         </label>
@@ -160,8 +160,8 @@
               <name>code</name>
               <mutable>True</mutable>
               <input_type>text</input_type>
-              <default_value></default_value>
-              <values></values>
+              <default_value />
+              <values />
             </attribute>
           </attributes>
         </label>
@@ -274,7 +274,7 @@
     </job>
     <dumped>2025-05-06 11:39:39.138521+00:00</dumped>
   </meta>
-  <image id="0" name="32a7d8aa5e0c78888082fdbf7e514cb283f0140e4af1144ee94f5a6b2bfb7424.pdf" width="2382" height="1587">
+  <image id="0" name="doc_32a7d8aa5e0c78888082fdbf7e514cb283f0140e4af1144ee94f5a6b2bfb7424_page_000001.png" width="2382" height="1587">
     <box label="page_header" source="file" occluded="0" xtl="1254.87" ytl="74.80" xbr="1277.90" ybr="86.65" z_order="0">
     </box>
     <box label="page_header" source="file" occluded="0" xtl="412.29" ytl="74.80" xbr="801.13" ybr="86.65" z_order="0">
@@ -284,7 +284,7 @@
     <box label="page_header" source="file" occluded="0" xtl="1105.89" ytl="74.80" xbr="1128.93" ybr="86.65" z_order="1">
     </box>
     <box label="picture" source="file" occluded="0" xtl="168.59" ytl="112.48" xbr="504.77" ybr="365.61" z_order="2">
-      <attribute name="json"></attribute>
+      <attribute name="json" />
     </box>
     <box label="table" source="file" occluded="0" xtl="1253.58" ytl="148.75" xbr="2296.11" ybr="248.12" z_order="2">
     </box>
@@ -304,14 +304,14 @@
     <box label="text" source="file" occluded="0" xtl="85.49" ytl="1305.32" xbr="588.75" ybr="1489.97" z_order="6">
     </box>
     <box label="picture" source="file" occluded="0" xtl="703.76" ytl="113.23" xbr="1053.09" ybr="379.42" z_order="7">
-      <attribute name="json"></attribute>
+      <attribute name="json" />
     </box>
     <box label="caption" source="manual" occluded="0" xtl="1254.87" ytl="314.20" xbr="1776.64" ybr="346.93" z_order="7">
     </box>
     <box label="table" source="file" occluded="0" xtl="1254.60" ytl="354.24" xbr="2296.30" ybr="648.77" z_order="8">
     </box>
     <box label="picture" source="file" occluded="0" xtl="695.13" ytl="393.20" xbr="1056.90" ybr="674.66" z_order="8">
-      <attribute name="json"></attribute>
+      <attribute name="json" />
     </box>
     <box label="text" source="file" occluded="0" xtl="624.65" ytl="771.24" xbr="1127.91" ybr="869.72" z_order="9">
     </box>
@@ -335,7 +335,7 @@
     <box label="text" source="file" occluded="0" xtl="1254.87" ytl="785.75" xbr="1758.13" ybr="1156.14" z_order="14">
     </box>
     <box label="picture" source="file" occluded="0" xtl="1336.64" ytl="1189.91" xbr="1676.35" ybr="1453.52" z_order="15">
-      <attribute name="json"></attribute>
+      <attribute name="json" />
     </box>
     <box label="section_header" source="file" occluded="0" xtl="1794.02" ytl="785.75" xbr="1914.54" ybr="800.57" z_order="16">
       <attribute name="level">1</attribute>
@@ -379,7 +379,7 @@
     <box label="text" source="file" occluded="0" xtl="1794.02" ytl="1427.44" xbr="2297.30" ybr="1484.09" z_order="21">
     </box>
   </image>
-  <image id="1" name="497a347f9b061036d72b5a52f54d4d36148755fa713e138bc8b2d1800157628d.pdf" width="2382" height="1587">
+  <image id="1" name="doc_497a347f9b061036d72b5a52f54d4d36148755fa713e138bc8b2d1800157628d_page_000001.png" width="2382" height="1587">
     <box label="page_header" source="file" occluded="0" xtl="412.29" ytl="74.80" xbr="801.13" ybr="86.65" z_order="0">
     </box>
     <box label="page_header" source="file" occluded="0" xtl="1254.87" ytl="74.80" xbr="1277.90" ybr="86.65" z_order="0">
@@ -389,16 +389,16 @@
     <box label="page_header" source="file" occluded="0" xtl="1105.89" ytl="74.80" xbr="1128.93" ybr="86.65" z_order="1">
     </box>
     <box label="picture" source="file" occluded="0" xtl="1436.43" ytl="111.88" xbr="2114.65" ybr="652.05" z_order="2">
-      <attribute name="json"></attribute>
+      <attribute name="json" />
     </box>
     <box label="picture" source="file" occluded="0" xtl="157.75" ytl="108.67" xbr="516.65" ybr="367.00" z_order="2">
-      <attribute name="json"></attribute>
+      <attribute name="json" />
     </box>
     <box label="section_header" source="file" occluded="0" xtl="1254.87" ytl="718.61" xbr="1437.00" ybr="733.42" z_order="3">
       <attribute name="level">2</attribute>
     </box>
     <box label="picture" source="file" occluded="0" xtl="157.63" ytl="384.86" xbr="516.71" ybr="646.69" z_order="3">
-      <attribute name="json"></attribute>
+      <attribute name="json" />
     </box>
     <box label="text" source="file" occluded="0" xtl="1254.87" ytl="760.44" xbr="1758.13" ybr="1130.83" z_order="4">
     </box>
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diff --git a/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml b/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml
index 4dd97fd..90447f9 100644
--- a/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml
+++ b/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml
@@ -1,4 +1,4 @@
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@@ -7,13 +7,13 @@
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@@ -26,7 +26,7 @@
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@@ -58,8 +58,8 @@
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@@ -102,8 +102,8 @@
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@@ -160,8 +160,8 @@
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+  </annotations>
\ No newline at end of file
diff --git a/tests/test_cvat.py b/tests/test_cvat.py
index c90ef82..5ea21a7 100644
--- a/tests/test_cvat.py
+++ b/tests/test_cvat.py
@@ -136,10 +136,13 @@ def run_cvat_e2e(idir: Path, odir: Path, annotation_xmlfile: Path):
     )
     assert os.path.exists(odir / "cvat_dataset_preannotated")
 
-    # Stage 3: copy the manual annotations
-    shutil.copy(
-        annotation_xmlfile, odir / "cvat_dataset_preannotated/cvat_annotations/xmls"
-    )
+    # Stage 3: copy the manual annotations to cvat_tasks directory with correct naming
+    # The modern parser expects XML files in cvat_tasks/ with pattern task_{xx}_set_A.xml
+    # Note: create_cvat creates images in task_00 (bucket 0) when bucket_size=10 and we have 4 PDFs
+    tasks_dir = odir / "cvat_dataset_preannotated" / "cvat_tasks"
+    tasks_dir.mkdir(parents=True, exist_ok=True)
+    # Copy to task_00_set_A.xml to match where images are actually created
+    shutil.copy(annotation_xmlfile, tasks_dir / "task_00_set_A.xml")
 
     # Stage 4: Create the dataset
     create_gt(
diff --git a/uv.lock b/uv.lock
index 0b45afb..f46c10c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -32,7 +32,7 @@ dependencies = [
     { name = "pyyaml" },
     { name = "safetensors" },
     { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/23/60/2757c4f03a8705dbf80b1268b03881927878dca5ed07d74f733fb6c219e0/accelerate-1.11.0.tar.gz", hash = "sha256:bb1caf2597b4cd632b917b5000c591d10730bb024a79746f1ee205bba80bd229", size = 393715, upload-time = "2025-10-20T14:42:25.025Z" }
 wheels = [
@@ -467,56 +467,56 @@ wheels = [
 
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+    { url = "https://files.pythonhosted.org/packages/03/52/ec01e9814379a049852a1eec7d0bd9afd92a3176cbc6ec54e9c70c1afe7f/boto3-1.40.72-py3-none-any.whl", hash = "sha256:1063a295712f2605d3e463e4dc1fe32fce17cf77a0f4d3bb14249d68533ee856", size = 139360, upload-time = "2025-11-12T20:34:26.995Z" },
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 [[package]]
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+version = "1.40.72"
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+    { url = "https://files.pythonhosted.org/packages/7a/ea/0f2814edc61c2e6fedd9b7a7fbc55149d1ffac7f7cd02d04cc51d1a3b1ca/boto3_stubs-1.40.72-py3-none-any.whl", hash = "sha256:4807f334b87914f75db3c6cd85f7eb706b5777e6ddaf117f8d63219cc01fb4b2", size = 68982, upload-time = "2025-11-12T20:36:12.855Z" },
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 [[package]]

From c71baa68aa8d9f033ee2bd2b581caeaff257c5a2 Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Thu, 20 Nov 2025 16:59:06 +0100
Subject: [PATCH 09/10] feat: combine subset datasets with tags and improve
 image format support

- Refactor cvat_deliveries_to_hf to build single combined dataset with subset tags
- Add tags field to DatasetRecord for subset tracking
- Add page counting to annotation task creation
- Support multipage TIFF and additional image formats (BMP, GIF)
- Add configurable JSON directory names to pipeline
- Fix caption/footnote target detection logic in CVAT converter

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 ...at_create_annotation_tasks_from_folders.py |   14 +-
 .../campaign_tools/cvat_deliveries_to_hf.py   |  231 ++--
 .../cvat_evaluation_pipeline.py               |   24 +-
 .../run_cvat_deliveries_pipeline.py           |   34 +-
 docling_eval/cvat_tools/cvat_to_docling.py    |   40 +-
 docling_eval/datamodels/dataset_record.py     |    8 +
 .../cvat_preannotation_builder.py             |    6 +
 .../dataset_builders/file_dataset_builder.py  |   88 +-
 docling_eval/utils/utils.py                   |   51 +
 uv.lock                                       | 1010 +++++++++--------
 10 files changed, 931 insertions(+), 575 deletions(-)

diff --git a/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py b/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py
index faa937c..ada90c8 100644
--- a/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py
+++ b/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py
@@ -32,6 +32,7 @@
 
 from docling_eval.cli.main import create_cvat, create_eval, create_gt
 from docling_eval.datamodels.types import BenchMarkNames, PredictionProviderType
+from docling_eval.utils.utils import count_pages_in_file
 
 app = typer.Typer(add_completion=False)
 
@@ -67,14 +68,19 @@ def process_subdirectories(
     for subdir in subdirs:
         subdir_name = subdir.name
 
-        # Count total files in subdirectory
-        total_files = 0
+        # Collect all files and count pages
+        all_files: list[Path] = []
         for ext in ["pdf", "tif", "tiff", "jpg", "jpeg", "png", "bmp", "gif", "json"]:
-            total_files += len(list(subdir.glob(f"*.{ext}")))
-            total_files += len(list(subdir.glob(f"*.{ext.upper()}")))
+            all_files.extend(subdir.glob(f"*.{ext}"))
+            all_files.extend(subdir.glob(f"*.{ext.upper()}"))
+        all_files.sort()
+
+        total_files = len(all_files)
+        total_pages = sum(count_pages_in_file(f) for f in all_files)
 
         typer.echo(f"\nProcessing: {subdir_name}")
         typer.echo(f"  Total files found: {total_files}")
+        typer.echo(f"  Total pages found: {total_pages}")
 
         # Calculate number of chunks needed
         num_chunks = (total_files + max_files_per_chunk - 1) // max_files_per_chunk
diff --git a/docling_eval/campaign_tools/cvat_deliveries_to_hf.py b/docling_eval/campaign_tools/cvat_deliveries_to_hf.py
index af88fd4..2032073 100644
--- a/docling_eval/campaign_tools/cvat_deliveries_to_hf.py
+++ b/docling_eval/campaign_tools/cvat_deliveries_to_hf.py
@@ -7,7 +7,7 @@
 from dataclasses import dataclass
 from enum import Enum
 from pathlib import Path
-from typing import Dict, List, Sequence
+from typing import Dict, Iterable, List, Sequence
 
 import typer
 
@@ -19,7 +19,7 @@
 app = typer.Typer(
     help=(
         "Aggregate DoclingDocument JSON exports produced by the CVAT delivery "
-        "pipeline into HuggingFace-ready parquet datasets, grouped by subset name."
+        "pipeline into a single HuggingFace-ready parquet dataset with subset tags."
     ),
     no_args_is_help=True,
     add_completion=False,
@@ -42,14 +42,14 @@ class ConfigEntry:
 
 
 @dataclass(frozen=True)
-class SubsetBuildStats:
-    name: str
-    submissions: int
+class CombinedBuildStats:
     record_count: int
+    submission_count: int
+    subsets: List[str]
 
     def as_config(self, dataset_dir_name: str, split: str) -> ConfigEntry:
-        pattern = f"{self.name}/{dataset_dir_name}/{split}/shard_*.parquet"
-        return ConfigEntry(name=self.name, split=split, path_pattern=pattern)
+        pattern = f"{dataset_dir_name}/{split}/shard_*.parquet"
+        return ConfigEntry(name="default", split=split, path_pattern=pattern)
 
 
 FEATURE_FIELD_RENDER: Dict[FieldType, tuple[str, str]] = {
@@ -109,23 +109,37 @@ def link_file(source: Path, destination: Path) -> None:
 
 def populate_staging_dir(
     staging_dir: Path,
-    source_dirs: Sequence[Path],
-) -> int:
+    subset_sources: Dict[str, List[Path]],
+) -> tuple[int, Dict[str, str]]:
+    """
+    Populate staging directory with files from all subsets.
+
+    Returns:
+        Tuple of (total_file_count, mapping from staged_filename to subset_name)
+    """
     file_index = 0
+    file_to_subset: Dict[str, str] = {}
 
-    for dir_idx, source_dir in enumerate(source_dirs):
-        json_files = sorted(p for p in source_dir.glob("*.json") if p.is_file())
-        if not json_files:
-            _LOGGER.warning("No JSON files found under %s", source_dir)
-            continue
-
-        for json_file in json_files:
-            link_name = f"{dir_idx:03d}_{file_index:06d}_{json_file.name}"
-            destination = staging_dir / link_name
-            link_file(json_file, destination)
-            file_index += 1
+    for subset_name in sorted(subset_sources.keys()):
+        for dir_idx, source_dir in enumerate(subset_sources[subset_name]):
+            json_files = sorted(p for p in source_dir.glob("*.json") if p.is_file())
+            if not json_files:
+                _LOGGER.warning("No JSON files found under %s", source_dir)
+                continue
+
+            for json_file in json_files:
+                # Prefix with subset name so we can extract it later
+                link_name = (
+                    f"{subset_name}_{dir_idx:03d}_{file_index:06d}_{json_file.name}"
+                )
+                destination = staging_dir / link_name
+                link_file(json_file, destination)
+                # Map the staged filename (without extension) to subset name
+                staged_stem = destination.stem
+                file_to_subset[staged_stem] = subset_name
+                file_index += 1
 
-    return file_index
+    return file_index, file_to_subset
 
 
 def read_num_rows(dataset_root: Path) -> int:
@@ -144,18 +158,38 @@ def read_num_rows(dataset_root: Path) -> int:
     return 0
 
 
-def build_subset_dataset(
-    subset_name: str,
-    subset_dirs: Sequence[Path],
-    staging_root: Path,
+def iter_records_with_tags(
+    builder: FileDatasetBuilder, file_to_subset: Dict[str, str]
+) -> Iterable[DatasetRecord]:
+    """
+    Iterate over records from FileDatasetBuilder and add subset tags.
+
+    The builder creates records with doc_id from filename.stem, so we match
+    the staged filename stem to find the subset name.
+    """
+    for record in builder.iterate():
+        # FileDatasetBuilder sets doc_id to filename.stem
+        # Match it to our file_to_subset mapping
+        subset_name = file_to_subset.get(record.doc_id)
+        if subset_name:
+            record.tags.append(f"subset:{subset_name}")
+        yield record
+
+
+def build_combined_dataset(
+    subset_sources: Dict[str, List[Path]],
+    staging_dir: Path,
     output_root: Path,
     dataset_dir_name: str,
     split: str,
     chunk_size: int,
     export_kind: DeliveryExportKind,
     force: bool,
-) -> SubsetBuildStats | None:
-    target_root = output_root / subset_name / dataset_dir_name
+) -> CombinedBuildStats | None:
+    """
+    Build a single combined dataset from all subsets with subset tags.
+    """
+    target_root = output_root / dataset_dir_name
     if target_root.exists():
         if not force:
             raise RuntimeError(
@@ -166,85 +200,117 @@ def build_subset_dataset(
         )
         shutil.rmtree(target_root)
 
-    ensure_clean_dir(staging_root / subset_name)
-    staging_dir = staging_root / subset_name
-    processed = populate_staging_dir(staging_dir, subset_dirs)
+    ensure_clean_dir(staging_dir)
+    processed, file_to_subset = populate_staging_dir(staging_dir, subset_sources)
     if processed == 0:
-        _LOGGER.warning("Subset %s had no JSON payloads. Skipping.", subset_name)
+        _LOGGER.warning("No JSON payloads found across all subsets. Skipping.")
         shutil.rmtree(staging_dir)
         return None
 
     builder = FileDatasetBuilder(
-        name=f"{subset_name}-{export_kind.value}",
+        name=f"combined-{export_kind.value}",
         dataset_source=staging_dir,
         target=target_root,
         split=split,
         file_extensions=["json"],
     )
-    builder.save_to_disk(chunk_size=chunk_size)
+
+    # Custom save logic that adds tags
+    from docling.utils.utils import chunkify
+
+    from docling_eval.utils.utils import save_shard_to_disk, write_datasets_info
+
+    test_dir = target_root / split
+    test_dir.mkdir(parents=True, exist_ok=True)
+
+    count = 0
+    chunk_count = 0
+
+    for record_chunk in chunkify(
+        iter_records_with_tags(builder, file_to_subset), chunk_size
+    ):
+        record_list = [r.as_record_dict() for r in record_chunk]
+        save_shard_to_disk(
+            items=record_list,
+            dataset_path=test_dir,
+            schema=DatasetRecord.pyarrow_schema(),
+            shard_id=chunk_count,
+        )
+        count += len(record_list)
+        chunk_count += 1
+
+    write_datasets_info(
+        name=builder.name,
+        output_dir=target_root,
+        num_train_rows=0,
+        num_test_rows=count,
+        features=DatasetRecord.features(),
+    )
+
     shutil.rmtree(staging_dir)
 
     record_count = read_num_rows(target_root)
-    submission_count = len(subset_dirs)
+    submission_count = sum(len(dirs) for dirs in subset_sources.values())
+    subset_names = sorted(subset_sources.keys())
 
-    return SubsetBuildStats(
-        name=subset_name,
-        submissions=submission_count,
+    return CombinedBuildStats(
         record_count=record_count if record_count else processed,
+        submission_count=submission_count,
+        subsets=subset_names,
     )
 
 
-def render_configs_block(configs: Sequence[ConfigEntry]) -> List[str]:
+def render_configs_block(config: ConfigEntry) -> List[str]:
     lines: List[str] = ["configs:"]
-    for entry in configs:
-        lines.append(f"- config_name: {entry.name}")
-        lines.append("  data_files:")
-        lines.append(f"    - split: {entry.split}")
-        lines.append(f"      path: {entry.path_pattern}")
+    lines.append(f"- config_name: {config.name}")
+    lines.append("  data_files:")
+    lines.append(f"    - split: {config.split}")
+    lines.append(f"      path: {config.path_pattern}")
     return lines
 
 
-def render_dataset_info_block(configs: Sequence[ConfigEntry]) -> List[str]:
+def render_dataset_info_block(config: ConfigEntry) -> List[str]:
     lines: List[str] = ["dataset_info:"]
     feature_rows = iter_dataset_features()
-    for entry in configs:
-        lines.append(f"- config_name: {entry.name}")
-        lines.append("  features:")
-        for feature_name, attr, value in feature_rows:
-            lines.append(f"  - name: {feature_name}")
-            lines.append(f"    {attr}: {value}")
-        lines.append("")
+    lines.append(f"- config_name: {config.name}")
+    lines.append("  features:")
+    for feature_name, attr, value in feature_rows:
+        lines.append(f"  - name: {feature_name}")
+        lines.append(f"    {attr}: {value}")
+    lines.append("")
     return lines
 
 
 def write_readme(
     output_root: Path,
-    configs: Sequence[ConfigEntry],
-    stats: Sequence[SubsetBuildStats],
+    config: ConfigEntry,
+    stats: CombinedBuildStats,
     license_name: str,
     export_kind: DeliveryExportKind,
 ) -> None:
     lines: List[str] = ["---"]
-    lines.extend(render_configs_block(configs))
-    lines.extend(render_dataset_info_block(configs))
+    lines.extend(render_configs_block(config))
+    lines.extend(render_dataset_info_block(config))
     lines.append(f"license: {license_name}")
     lines.append("---")
     lines.append("")
     lines.append("# CVAT Delivery Aggregation")
     lines.append(
         "This repository consolidates DoclingDocument exports produced by the "
-        "CVAT delivery pipeline into HuggingFace-ready parquet datasets."
+        "CVAT delivery pipeline into a single HuggingFace-ready parquet dataset."
     )
     lines.append("")
     lines.append(f"- Source payloads: `{export_kind.folder_name()}`")
     lines.append("- Builder: `FileDatasetBuilder` with JSON inputs")
+    lines.append(
+        "- Subset information: Each record includes a `tags` field with "
+        "`subset:<name>` entries indicating the source subset"
+    )
     lines.append("")
-    lines.append("## Subsets")
-    for subset in stats:
-        lines.append(
-            f"- `{subset.name}`: {subset.record_count} documents from "
-            f"{subset.submissions} submissions"
-        )
+    lines.append("## Dataset Statistics")
+    lines.append(f"- Total records: {stats.record_count}")
+    lines.append(f"- Total submissions: {stats.submission_count}")
+    lines.append(f"- Subsets included: {', '.join(f'`{s}`' for s in stats.subsets)}")
     readme_path = output_root / "README.md"
     with readme_path.open("w", encoding="utf-8") as handle:
         handle.write("\n".join(lines).rstrip() + "\n")
@@ -270,7 +336,7 @@ def main(
     dataset_dir_name: str = typer.Option(
         "gt_dataset",
         "--dataset-dir-name",
-        help="Name of the dataset directory created inside each subset folder.",
+        help="Name of the dataset directory created in the output folder.",
     ),
     chunk_size: int = typer.Option(
         80,
@@ -285,7 +351,7 @@ def main(
     force: bool = typer.Option(
         False,
         "--force/--no-force",
-        help="Overwrite any existing subset dataset directories and staging data.",
+        help="Overwrite any existing dataset directory and staging data.",
     ),
 ) -> None:
     logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
@@ -308,33 +374,32 @@ def main(
         )
         raise typer.Exit(code=1)
 
-    built_stats: List[SubsetBuildStats] = []
-    for subset_name in sorted(subset_sources.keys()):
-        stats = build_subset_dataset(
-            subset_name=subset_name,
-            subset_dirs=sorted(subset_sources[subset_name]),
-            staging_root=staging_root,
-            output_root=output_dir,
-            dataset_dir_name=dataset_dir_name,
-            split=split,
-            chunk_size=chunk_size,
-            export_kind=export_kind,
-            force=force,
-        )
-        if stats:
-            built_stats.append(stats)
+    # Sort subset sources for deterministic ordering
+    sorted_subset_sources = {k: sorted(v) for k, v in sorted(subset_sources.items())}
+
+    staging_dir = staging_root / "combined"
+    stats = build_combined_dataset(
+        subset_sources=sorted_subset_sources,
+        staging_dir=staging_dir,
+        output_root=output_dir,
+        dataset_dir_name=dataset_dir_name,
+        split=split,
+        chunk_size=chunk_size,
+        export_kind=export_kind,
+        force=force,
+    )
 
     shutil.rmtree(staging_root, ignore_errors=True)
 
-    if not built_stats:
+    if not stats:
         typer.echo("No datasets were produced.", err=True)
         raise typer.Exit(code=1)
 
-    configs = [stat.as_config(dataset_dir_name, split) for stat in built_stats]
+    config = stats.as_config(dataset_dir_name, split)
     write_readme(
         output_root=output_dir,
-        configs=configs,
-        stats=built_stats,
+        config=config,
+        stats=stats,
         license_name=license_name,
         export_kind=export_kind,
     )
diff --git a/docling_eval/campaign_tools/cvat_evaluation_pipeline.py b/docling_eval/campaign_tools/cvat_evaluation_pipeline.py
index 6c86126..75febf7 100755
--- a/docling_eval/campaign_tools/cvat_evaluation_pipeline.py
+++ b/docling_eval/campaign_tools/cvat_evaluation_pipeline.py
@@ -84,6 +84,8 @@ def __init__(
         force_ocr: bool = False,
         ocr_scale: float = 1.0,
         storage_scale: float = 2.0,
+        gt_json_dirname: str = "ground_truth_json",
+        pred_json_dirname: str = "predictions_json",
     ):
         """
         Initialize the pipeline.
@@ -97,6 +99,8 @@ def __init__(
             ocr_scale: Scale factor for rendering PDFs for OCR (default: 1.0 = 72 DPI).
                       Higher values increase OCR resolution. Coordinates are mapped back to storage_scale.
             storage_scale: Scale for stored page images and coordinates (default: 2.0 for 144 DPI).
+            gt_json_dirname: Name of the subdirectory for ground truth JSON exports (default: "ground_truth_json")
+            pred_json_dirname: Name of the subdirectory for prediction JSON exports (default: "predictions_json")
         """
         self.cvat_root = Path(cvat_root)
         self.output_dir = Path(output_dir)
@@ -108,8 +112,8 @@ def __init__(
         self._folder_cache: Dict[str, CVATFolderStructure] = {}
 
         # Create subdirectories
-        self.gt_json_dir = self.output_dir / "ground_truth_json"
-        self.pred_json_dir = self.output_dir / "predictions_json"
+        self.gt_json_dir = self.output_dir / gt_json_dirname
+        self.pred_json_dir = self.output_dir / pred_json_dirname
         self.gt_dataset_dir = self.output_dir / "gt_dataset"
         self.eval_dataset_dir = self.output_dir / "eval_dataset"
         self.evaluation_results_dir = self.output_dir / "evaluation_results"
@@ -843,6 +847,20 @@ def main():
         help="Reuse the existing eval_dataset parquet shards when present.",
     )
 
+    parser.add_argument(
+        "--gt-json-dirname",
+        type=str,
+        default="ground_truth_json",
+        help="Directory name for ground truth JSON exports (default: ground_truth_json).",
+    )
+
+    parser.add_argument(
+        "--pred-json-dirname",
+        type=str,
+        default="predictions_json",
+        help="Directory name for prediction JSON exports (default: predictions_json).",
+    )
+
     args = parser.parse_args()
 
     if args.verbose:
@@ -881,6 +899,8 @@ def _validate_dir(path: Path, name: str) -> None:
         force_ocr=args.force_ocr,
         ocr_scale=args.ocr_scale,
         storage_scale=args.storage_scale,
+        gt_json_dirname=args.gt_json_dirname,
+        pred_json_dirname=args.pred_json_dirname,
     )
 
     # Execute requested pipeline step
diff --git a/docling_eval/campaign_tools/run_cvat_deliveries_pipeline.py b/docling_eval/campaign_tools/run_cvat_deliveries_pipeline.py
index 3bc4042..b809064 100644
--- a/docling_eval/campaign_tools/run_cvat_deliveries_pipeline.py
+++ b/docling_eval/campaign_tools/run_cvat_deliveries_pipeline.py
@@ -102,6 +102,8 @@ def should_skip_job(
         *,
         resume_from_json: bool,
         stop_after_json: bool,
+        gt_json_dirname: str,
+        pred_json_dirname: str,
     ) -> tuple[bool, str]:
         """Determine if a job should be skipped.
 
@@ -109,6 +111,8 @@ def should_skip_job(
             job: The submission subset job to check
             resume_from_json: Whether the run is resuming from prior JSON exports
             stop_after_json: Whether the current execution stops after JSON generation
+            gt_json_dirname: Directory name for ground truth JSON exports
+            pred_json_dirname: Directory name for prediction JSON exports
 
         Returns:
             Tuple of (should_skip, reason_message)
@@ -131,8 +135,8 @@ def should_skip_job(
             if stop_after_json:
                 required_outputs.extend(
                     [
-                        (job.output_dir / "ground_truth_json", "ground_truth_json"),
-                        (job.output_dir / "predictions_json", "predictions_json"),
+                        (job.output_dir / gt_json_dirname, gt_json_dirname),
+                        (job.output_dir / pred_json_dirname, pred_json_dirname),
                     ]
                 )
             else:
@@ -228,6 +232,8 @@ def _execute_job(
     stop_after_json: bool,
     resume_from_json: bool,
     reuse_eval_dataset: bool,
+    gt_json_dirname: str,
+    pred_json_dirname: str,
 ) -> Optional[pd.DataFrame]:
     """Execute pipeline stages for a single job according to the execution plan.
 
@@ -239,6 +245,8 @@ def _execute_job(
         force_ocr: Force OCR on PDF pages
         ocr_scale: Scale factor for OCR rendering
         storage_scale: Scale for stored page images and coordinates
+        gt_json_dirname: Directory name for ground truth JSON exports
+        pred_json_dirname: Directory name for prediction JSON exports
 
     Returns:
         DataFrame with evaluation results, or None if no evaluation was run
@@ -251,6 +259,8 @@ def _execute_job(
         force_ocr=force_ocr,
         ocr_scale=ocr_scale,
         storage_scale=storage_scale,
+        gt_json_dirname=gt_json_dirname,
+        pred_json_dirname=pred_json_dirname,
     )
 
     job.output_dir.mkdir(parents=True, exist_ok=True)
@@ -450,6 +460,8 @@ def run_jobs(
     stop_after_json: bool = False,
     resume_from_json: bool = False,
     reuse_eval_dataset: bool = False,
+    gt_json_dirname: str = "ground_truth_json",
+    pred_json_dirname: str = "predictions_json",
 ) -> None:
     """Execute the CVAT evaluation pipeline for each prepared job."""
     if not jobs:
@@ -507,6 +519,8 @@ def run_jobs(
                 job,
                 resume_from_json=resume_from_json,
                 stop_after_json=stop_after_json,
+                gt_json_dirname=gt_json_dirname,
+                pred_json_dirname=pred_json_dirname,
             )
             if should_skip:
                 _LOGGER.info("  ⊘ Skipped: %s", skip_reason)
@@ -537,6 +551,8 @@ def run_jobs(
                     stop_after_json=stop_after_json,
                     resume_from_json=resume_from_json,
                     reuse_eval_dataset=reuse_eval_dataset,
+                    gt_json_dirname=gt_json_dirname,
+                    pred_json_dirname=pred_json_dirname,
                 )
 
                 if subset_df is not None and not subset_df.empty:
@@ -763,6 +779,18 @@ def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace:
         action="store_true",
         help="Reuse existing eval_dataset parquet shards when present.",
     )
+    parser.add_argument(
+        "--gt-json-dirname",
+        type=str,
+        default="ground_truth_json",
+        help="Directory name for ground truth JSON exports (default: ground_truth_json).",
+    )
+    parser.add_argument(
+        "--pred-json-dirname",
+        type=str,
+        default="predictions_json",
+        help="Directory name for prediction JSON exports (default: predictions_json).",
+    )
 
     return parser.parse_args(argv)
 
@@ -795,6 +823,8 @@ def main(argv: Optional[Sequence[str]] = None) -> None:
             stop_after_json=args.stop_after_json,
             resume_from_json=args.resume_from_json,
             reuse_eval_dataset=args.reuse_eval_dataset,
+            gt_json_dirname=args.gt_json_dirname,
+            pred_json_dirname=args.pred_json_dirname,
         )
     except ValueError as exc:
         _LOGGER.error("%s", exc)
diff --git a/docling_eval/cvat_tools/cvat_to_docling.py b/docling_eval/cvat_tools/cvat_to_docling.py
index b48a71a..dc8365b 100644
--- a/docling_eval/cvat_tools/cvat_to_docling.py
+++ b/docling_eval/cvat_tools/cvat_to_docling.py
@@ -1318,18 +1318,34 @@ def _has_valid_container_relationship(
     def _is_caption_or_footnote_target(self, element_id: int) -> bool:
         """Check if element is a target of a VALID caption/footnote relationship.
 
-        Only returns True if the element is a target AND at least one side of the
-        relationship is a container element. This prevents non-container elements
-        from being skipped when they're part of invalid caption/footnote paths.
+        Only returns True if:
+        1. The element is in the target position (caption/footnote side)
+        2. AND the container position element is actually a container
+        3. AND the target element is NOT itself a container
+
+        This prevents containers from being skipped when they're incorrectly placed
+        in the target position of invalid caption/footnote paths.
         """
+        from .utils import is_caption_element, is_container_element, is_footnote_element
+
         # Check captions
         for path_id, (
             container_id,
             caption_id,
         ) in self.doc_structure.path_mappings.to_caption.items():
             if caption_id == element_id:
-                if self._has_valid_container_relationship(container_id, caption_id):
-                    return True
+                # Get the actual elements
+                container_el = self.doc_structure.get_element_by_id(container_id)
+                caption_el = self.doc_structure.get_element_by_id(caption_id)
+
+                if container_el and caption_el:
+                    # Only skip if:
+                    # 1. Container position has an actual container
+                    # 2. Caption position is NOT a container (would be invalid)
+                    if is_container_element(container_el) and not is_container_element(
+                        caption_el
+                    ):
+                        return True
 
         # Check footnotes
         for path_id, (
@@ -1337,8 +1353,18 @@ def _is_caption_or_footnote_target(self, element_id: int) -> bool:
             footnote_id,
         ) in self.doc_structure.path_mappings.to_footnote.items():
             if footnote_id == element_id:
-                if self._has_valid_container_relationship(container_id, footnote_id):
-                    return True
+                # Get the actual elements
+                container_el = self.doc_structure.get_element_by_id(container_id)
+                footnote_el = self.doc_structure.get_element_by_id(footnote_id)
+
+                if container_el and footnote_el:
+                    # Only skip if:
+                    # 1. Container position has an actual container
+                    # 2. Footnote position is NOT a container (would be invalid)
+                    if is_container_element(container_el) and not is_container_element(
+                        footnote_el
+                    ):
+                        return True
 
         return False
 
diff --git a/docling_eval/datamodels/dataset_record.py b/docling_eval/datamodels/dataset_record.py
index 6b6104a..d72a27a 100644
--- a/docling_eval/datamodels/dataset_record.py
+++ b/docling_eval/datamodels/dataset_record.py
@@ -100,6 +100,7 @@ class DatasetRecord(
 
     mime_type: str = Field(default="application/pdf")
     modalities: List[EvaluationModality] = Field(default=[])
+    tags: List[str] = Field(alias="tags", default_factory=list)
 
     model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True)
 
@@ -121,6 +122,7 @@ def _get_field_definitions(cls) -> Dict[str, FieldType]:
             cls.get_field_alias("original"): FieldType.BINARY,
             cls.get_field_alias("mime_type"): FieldType.STRING,
             cls.get_field_alias("modalities"): FieldType.STRING_LIST,
+            cls.get_field_alias("tags"): FieldType.STRING_LIST,
         }
 
     @classmethod
@@ -167,6 +169,7 @@ def as_record_dict(self):
             self.get_field_alias("modalities"): list(
                 [m.value for m in self.modalities]
             ),
+            self.get_field_alias("tags"): self.tags,
         }
         if isinstance(self.original, Path):
             with self.original.open("rb") as f:
@@ -237,6 +240,11 @@ def validate_record_dict(cls, data: dict):
                 img_buffer.seek(0)
                 data[gt_binary] = DocumentStream(name="image.png", stream=img_buffer)
 
+        # Backward compatibility: ensure tags field exists for old datasets
+        tags_alias = cls.get_field_alias("tags")
+        if tags_alias not in data:
+            data[tags_alias] = []
+
         return data
 
 
diff --git a/docling_eval/dataset_builders/cvat_preannotation_builder.py b/docling_eval/dataset_builders/cvat_preannotation_builder.py
index bd9ce86..ff24837 100644
--- a/docling_eval/dataset_builders/cvat_preannotation_builder.py
+++ b/docling_eval/dataset_builders/cvat_preannotation_builder.py
@@ -269,6 +269,12 @@ def _export_from_dataset(self) -> AnnotationOverview:
                     bin_ext = ".png"
                 elif mime_type in ["image/jpg", "image/jpeg"]:
                     bin_ext = ".jpg"
+                elif mime_type in ["image/tiff", "image/tif"]:
+                    bin_ext = ".tiff"
+                elif mime_type == "image/bmp":
+                    bin_ext = ".bmp"
+                elif mime_type == "image/gif":
+                    bin_ext = ".gif"
                 else:
                     _log.warning(
                         f"Unsupported mime-type {mime_type}, using .bin extension"
diff --git a/docling_eval/dataset_builders/file_dataset_builder.py b/docling_eval/dataset_builders/file_dataset_builder.py
index 262daa5..314f602 100644
--- a/docling_eval/dataset_builders/file_dataset_builder.py
+++ b/docling_eval/dataset_builders/file_dataset_builder.py
@@ -114,22 +114,78 @@ def iterate(self) -> Iterable[DatasetRecord]:
                     pdf_path=filename, true_doc=true_doc, image_scale=2.0
                 )
             elif mime_type is not None and mime_type.startswith("image/"):
-                image: Image.Image = Image.open(filename)
-                image = image.convert("RGB")
-                image_ref = ImageRef(
-                    mimetype="image/png",
-                    dpi=72,
-                    size=Size(width=image.width, height=image.height),
-                    uri=from_pil_to_base64uri(image),
-                )
-                page_item = PageItem(
-                    page_no=1,
-                    size=Size(width=float(image.width), height=float(image.height)),
-                    image=image_ref,
-                )
-
-                # _log.debug(f"add_pages_to_true_doc: {filename}")
-                true_doc.pages[1] = page_item
+                # Handle multipage TIFF files
+                if filename.suffix.lower() in [".tif", ".tiff"]:
+                    # Process all pages/frames in multipage TIFF
+                    page_no = 1
+                    try:
+                        img = Image.open(filename)
+                        while True:
+                            img.seek(page_no - 1)
+                            image = img.convert("RGB")
+                            image_ref = ImageRef(
+                                mimetype="image/png",
+                                dpi=72,
+                                size=Size(width=image.width, height=image.height),
+                                uri=from_pil_to_base64uri(image),
+                            )
+                            page_item = PageItem(
+                                page_no=page_no,
+                                size=Size(
+                                    width=float(image.width),
+                                    height=float(image.height),
+                                ),
+                                image=image_ref,
+                            )
+                            true_doc.pages[page_no] = page_item
+                            page_no += 1
+                            # Try to seek to next frame
+                            try:
+                                img.seek(page_no - 1)
+                            except EOFError:
+                                # No more frames
+                                break
+                        img.close()
+                    except Exception as e:
+                        _log.warning(
+                            f"Failed to process multipage TIFF {filename}: {e}. "
+                            "Falling back to single-page processing."
+                        )
+                        # Fallback to single-page processing
+                        image = Image.open(filename)
+                        image = image.convert("RGB")
+                        image_ref = ImageRef(
+                            mimetype="image/png",
+                            dpi=72,
+                            size=Size(width=image.width, height=image.height),
+                            uri=from_pil_to_base64uri(image),
+                        )
+                        page_item = PageItem(
+                            page_no=1,
+                            size=Size(
+                                width=float(image.width), height=float(image.height)
+                            ),
+                            image=image_ref,
+                        )
+                        true_doc.pages[1] = page_item
+                else:
+                    # Single-page image formats
+                    image = Image.open(filename)
+                    image = image.convert("RGB")
+                    image_ref = ImageRef(
+                        mimetype="image/png",
+                        dpi=72,
+                        size=Size(width=image.width, height=image.height),
+                        uri=from_pil_to_base64uri(image),
+                    )
+                    page_item = PageItem(
+                        page_no=1,
+                        size=Size(width=float(image.width), height=float(image.height)),
+                        image=image_ref,
+                    )
+
+                    # _log.debug(f"add_pages_to_true_doc: {filename}")
+                    true_doc.pages[1] = page_item
             elif mime_type == "application/json":
                 # .. support DoclingDocument json files
                 try:
diff --git a/docling_eval/utils/utils.py b/docling_eval/utils/utils.py
index a512cf0..5e19e4c 100644
--- a/docling_eval/utils/utils.py
+++ b/docling_eval/utils/utils.py
@@ -51,6 +51,57 @@ def get_binhash(binary_data: bytes) -> str:
     return hash_hex
 
 
+def count_pages_in_file(file_path: Path) -> int:
+    """
+    Count the number of pages in a file.
+
+    For PDFs, counts actual pages. For multipage TIFFs, counts frames/pages.
+    For single-page images and other files, returns 1.
+
+    Args:
+        file_path: Path to the file
+
+    Returns:
+        Number of pages/frames in the file
+    """
+    import mimetypes
+
+    mime_type, _ = mimetypes.guess_type(file_path)
+
+    if mime_type == "application/pdf":
+        try:
+            in_doc = get_input_document(file_path, backend_t=PyPdfiumDocumentBackend)
+            if in_doc.valid:
+                return in_doc.page_count
+            return 1
+        except Exception as e:
+            logging.warning(f"Failed to count pages in PDF {file_path}: {e}")
+            return 1
+    elif mime_type is not None and mime_type.startswith("image/"):
+        # Check if it's a TIFF file (which can be multipage)
+        if file_path.suffix.lower() in [".tif", ".tiff"]:
+            try:
+                with Image.open(file_path) as img:
+                    # Try to get the number of frames/pages
+                    page_count = 0
+                    try:
+                        while True:
+                            img.seek(page_count)
+                            page_count += 1
+                    except EOFError:
+                        pass
+                    return max(1, page_count)  # At least 1 page
+            except Exception as e:
+                logging.warning(f"Failed to count pages in TIFF {file_path}: {e}")
+                return 1
+        else:
+            # Single-page image formats
+            return 1
+    else:
+        # Other file types (JSON, etc.) are treated as single-page
+        return 1
+
+
 def write_datasets_info(
     name: str,
     output_dir: Path,
diff --git a/uv.lock b/uv.lock
index f46c10c..2e0ba2b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -26,7 +26,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "numpy", version = "2.3.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
@@ -389,8 +389,7 @@ name = "black"
 version = "24.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", version = "8.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "click", version = "8.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "click" },
     { name = "mypy-extensions" },
     { name = "packaging" },
     { name = "pathspec" },
@@ -467,65 +466,65 @@ wheels = [
 
 [[package]]
 name = "boto3"
-version = "1.40.72"
+version = "1.40.75"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "botocore" },
     { name = "jmespath" },
     { name = "s3transfer" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/32/23/a2015ca755f11cbc3080aa1fe17af3c97225388aa4a10de1a22b1ae46152/boto3-1.40.72.tar.gz", hash = "sha256:58d30dd5e046789a760db7a49f817650b8ff08d8d169e127976a61f44b7c59ad", size = 111618, upload-time = "2025-11-12T20:34:29.03Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/8f/2c/0a6e49612ef9868382ef76292da2dd1dee7c74a0f1ec95323e76f6e2ef4b/boto3-1.40.75.tar.gz", hash = "sha256:a5219a2f397f8616462d7908e696c281f120aa2d8458280ff24f7ddeb2108faf", size = 111629, upload-time = "2025-11-17T21:58:37.667Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/03/52/ec01e9814379a049852a1eec7d0bd9afd92a3176cbc6ec54e9c70c1afe7f/boto3-1.40.72-py3-none-any.whl", hash = "sha256:1063a295712f2605d3e463e4dc1fe32fce17cf77a0f4d3bb14249d68533ee856", size = 139360, upload-time = "2025-11-12T20:34:26.995Z" },
+    { url = "https://files.pythonhosted.org/packages/65/85/2b0ea3ca19447d3a681b59b712a8f7861bfd0bc0129efd8a2da09d272837/boto3-1.40.75-py3-none-any.whl", hash = "sha256:c246fb35d9978b285c5b827a20b81c9e77d52f99c9d175fbd91f14396432953f", size = 139360, upload-time = "2025-11-17T21:58:36.181Z" },
 ]
 
 [[package]]
 name = "boto3-stubs"
-version = "1.40.72"
+version = "1.40.75"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "botocore-stubs" },
     { name = "types-s3transfer" },
     { name = "typing-extensions", marker = "python_full_version < '3.12'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4f/db/90881ac0b8afdfa9b95ae66b4094ed33f88b6086a8945229a95156257ca9/boto3_stubs-1.40.72.tar.gz", hash = "sha256:cbcf7b6e8a7f54e77fcb2b8d00041993fe4f76554c716b1d290e48650d569cd0", size = 99406, upload-time = "2025-11-12T20:36:23.685Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f4/86/91167946eec229012960136aae62218cd27f21fa867a9ef3eae16e6ac561/boto3_stubs-1.40.75.tar.gz", hash = "sha256:5bba917ad4b2d905a6ae4cf07ac5e82bdd06815a25b74aec738e9115bd76d8a3", size = 99529, upload-time = "2025-11-17T22:17:37.385Z" }
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+    { name = "tree-sitter-javascript" },
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 name = "triton"
 version = "3.4.0"
@@ -7256,8 +7345,7 @@ name = "typer"
 version = "0.19.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", version = "8.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "click", version = "8.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "click" },
     { name = "rich" },
     { name = "shellingham" },
     { name = "typing-extensions" },
@@ -7386,7 +7474,7 @@ name = "uvicorn"
 version = "0.38.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", version = "8.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "click", marker = "python_full_version < '3.14'" },
     { name = "h11", marker = "python_full_version < '3.14'" },
     { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]

From 33f301d99784f502563fd44ff0d3fb501e96ae5d Mon Sep 17 00:00:00 2001
From: Christoph Auer <cau@zurich.ibm.com>
Date: Thu, 20 Nov 2025 17:02:56 +0100
Subject: [PATCH 10/10] Update review bundle README

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
---
 .../campaign_tools/README_review_bundle.md    | 23 +++++--------------
 1 file changed, 6 insertions(+), 17 deletions(-)

diff --git a/docling_eval/campaign_tools/README_review_bundle.md b/docling_eval/campaign_tools/README_review_bundle.md
index 5cc6460..2d47a78 100644
--- a/docling_eval/campaign_tools/README_review_bundle.md
+++ b/docling_eval/campaign_tools/README_review_bundle.md
@@ -7,7 +7,7 @@ Follow these steps whenever you need to review a submission’s visualization ou
 You need to start from a submission evaluation directory, e.g. `submission-4_10-11-2025_v0`, as created through the `cvat_evaluation_pipeline.py`.
 It is required to contain a `combined_evaluation.csv` (export it from `combined_evaluation.xlsx` if missing).
 
-Run the bundle builder from the submission root. This reads `combined_evaluation.csv` and writes a timestamped bundle folder next to it. Both `need_review` and `need_task2_review` are included automatically in every bundle, so you can switch between them later without extra arguments.
+Run the bundle builder from the submission root. This reads `combined_evaluation.csv` and writes a timestamped bundle folder next to it.
 
 ```bash
 uv run python -m docling_eval.campaign_tools.review_bundle_builder \
@@ -16,17 +16,6 @@ uv run python -m docling_eval.campaign_tools.review_bundle_builder \
 
 The command prints the bundle path, e.g. `submission-4_10-11-2025_v0/review_bundle_20251110_162408`.
 
-### Update an existing bundle in-place
-
-If you already have progress in `review_state.json`, regenerate the manifest and static assets without touching that file by passing `--bundle-dir`. When `--bundle-dir` is supplied you can omit the positional `submission_dir`; the tool automatically resolves it to the bundle’s parent directory:
-
-```bash
-uv run python -m docling_eval.campaign_tools.review_bundle_builder \
-  --bundle-dir /path/to/submission-root/review_bundle_20251110_162408
-```
-
-This keeps `review_state.json` intact while refreshing the HTML/JS/CSS.
-
 ## 2. Host the Submission Root
 
 Serve the submission directory so the bundle and original visualization HTMLs share the same origin:
@@ -38,7 +27,7 @@ python -m http.server 8765
 
 Open `http://localhost:8765/review_bundle_*/index.html` in your browser.
 
-## 3. Connect `review_state.json`
+## 3. Connect to `review_state.json` database
 
 At the top of the page click **Connect bundle for saving**. Choose the same `review_bundle_*` folder. **This is critical** to ensure the browser has permissions to save into the `review_state.json`, which acts as a database. Once connected, the button switches to “Change bundle connection” and the status line on the bottom states that saves go directly to `review_state.json`. Decisions are still mirrored in browser storage as a safety net.
 
@@ -48,9 +37,9 @@ You can import prior logs via **Import log** (JSON or CSV). Imports do not chang
 
 The left sidebar lists documents sorted by the configured column. Selecting an entry loads its visualization(s). You will find the tab buttons for key-value and layout if both are available. The decision panel shows separate controls for **User A** and **User B**:
 
-1. Use the **Priority** dropdown above the list to switch between `need_review`, `need_task2_review`, or any other column you exposed during bundle creation.
-2. Click **Correct** or **Need changes** for each user as needed (buttons stay lit when active).
-3. Optionally add a shared comment.
-4. Press **Save decision**. The status line confirms whether you saved user verdicts, comments only, or cleared the entry.
+1. Click **Correct** or **Need changes** for each user as needed (buttons stay lit when active).
+2. Optionally add a shared comment.
+3. Press **Save decision**. The status line confirms whether you saved user verdicts, comments only, or cleared the entry.
 
 Edits remain locked to the current sample until you save or discard them; this prevents accidental navigation losses. Use **Export CSV** at any time for an external report.
+