Skip to content

How to optimally prepare the data #144

@lorenzob

Description

@lorenzob

Hi, I'd like to know what is the recommended/optimal data preparation for training (and recognition, if different).

For example:

  • is it better to use a grayscale image or a binary one?
  • is it better to leave some white margins (left/right, top/bottom) or trim tight to the text(*)? In the first case, the "target_height" should include the margin or not?
  • does it perform some kind or text straightening/dewarping or should I do it?
  • any other things to consider?

Thanks.

(*) I'm asking this because when I started to using it, it was common for the very first letter to be discarded and adding some white margin seemed to fix it. But I was using very little data and maybe it was just a coincidence. The uw3 samples also have a small border.

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions