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sbb_binarization/README.md

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# Binarization
> Binarization for document images
## Examples
<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">
## Introduction
This tool performs document image binarization (i.e. transform colour/grayscale
to black-and-white pixels) for OCR using multiple trained models.
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)_.
## Installation
Clone the repository, enter it and run
`pip install .`
### Models
Pre-trained models can be downloaded from here:
https://qurator-data.de/sbb_binarization/
## Usage
```sh
sbb_binarize \
--patches \
-m <directory with models> \
<input image> \
<output image>
```
**Note** In virtually all cases, the `--patches` flag will improve results.