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# Binarization
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> Binarization for document images
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## Examples
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<img src="https://user-images.githubusercontent.com/952378/63592437-e433e400-c5b1-11e9-9c2d-889c6e93d748.jpg" width="180"><img src="https://user-images.githubusercontent.com/952378/63592435-e433e400-c5b1-11e9-88e4-3e441b61fa67.jpg" width="180"><img src="https://user-images.githubusercontent.com/952378/63592440-e4cc7a80-c5b1-11e9-8964-2cd1b22c87be.jpg" width="220"><img src="https://user-images.githubusercontent.com/952378/63592438-e4cc7a80-c5b1-11e9-86dc-a9e9f8555422.jpg" width="220">
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## Introduction
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This tool performs document image binarization using a trained ResNet50-UNet model.
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## Installation
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Clone the repository, enter it and run
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`pip install .`
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### Models
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Pre-trained models in `HDF5` format can be downloaded from here:
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https://qurator-data.de/sbb_binarization/
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We also provide a Tensorflow `saved_model` via Huggingface:
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https://huggingface.co/SBB/sbb_binarization
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## Usage
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```sh
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sbb_binarize \
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-m <path to directory containing model files \
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<input image> \
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<output image>
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```
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Images containing a lot of border noise (black pixels) should be cropped beforehand to improve the quality of results.
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### Example
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```sh
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sbb_binarize -m /path/to/model/ myimage.tif myimage-bin.tif
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```
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To use the [OCR-D](https://ocr-d.de/) interface:
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```sh
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ocrd-sbb-binarize --overwrite -I INPUT_FILE_GRP -O OCR-D-IMG-BIN -P model "/var/lib/sbb_binarization"
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```
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