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39 lines
1.2 KiB
Markdown
39 lines
1.2 KiB
Markdown
# 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 (i.e. transform colour/grayscale
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to black-and-white pixels) for OCR using multiple trained models.
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The method used is based on _Calvo-Zaragoza/Gallego, 2018. [A selectional auto-encoder approach for document image binarization](https://arxiv.org/abs/1706.10241)_.
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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 can be downloaded from here:
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https://qurator-data.de/sbb_binarization/
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## Usage
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```sh
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sbb_binarize \
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--patches \
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-m <directory with models> \
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<input image> \
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<output image>
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```
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**Note** In virtually all cases, the `--patches` flag will improve results.
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