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README.md

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 h5 format can be downloaded from here:

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

We also provide a Tensorflow saved_model via Huggingface:

https://huggingface.co/SBB/sbb_binarization

Usage

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

In virtually all cases, applying the --patches flag will improve the quality of results.

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

Example

sbb_binarize --patches -m /path/to/models/ myimage.tif myimage-bin.tif

To use the OCR-D interface:

ocrd-sbb-binarize --overwrite -I INPUT_FILE_GRP -O OCR-D-IMG-BIN -P model "/var/lib/sbb_binarization"