Binarization

Binarization for document images

Examples

Introduction

This tool performs document image binarization using a trained ResNet50-UNet model.

Installation

Clone the repository, enter it and run

pip install .

Models

Pre-trained models in HDF5 format can be downloaded from here:

https://qurator-data.de/sbb_binarization/

We also provide models in Tensorflow SavedModel format via Huggingface and Github release assets:

https://huggingface.co/SBB/sbb_binarization https://github.com/qurator-spk/sbb_binarization/releases

With OCR-D, you can use the [Resource Manager](Tensorflow SavedModel) to deploy models, e.g.

ocrd resmgr download ocrd-sbb-binarize "*"

Usage

sbb_binarize \
  -m <path to directory containing model files \
  <input image> \
  <output image>

Images containing a lot of border noise (black pixels) should be cropped beforehand to improve the quality of results.

Example

sbb_binarize -m /path/to/model/ myimage.tif myimage-bin.tif

To use the OCR-D interface:

ocrd-sbb-binarize -I INPUT_FILE_GRP -O OCR-D-IMG-BIN -P model default

Testing

For simple smoke tests, the following will

• download models

• download test data

• run the OCR-D wrapper (on page and region level):

    make model
    make test