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# Binarization
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2020-10-15 14:44:41 +02:00
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2020-01-15 19:49:18 +01:00
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> Binarization for document images
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2019-12-10 13:00:05 +01:00
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2020-10-29 00:45:55 +01:00
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# Examples
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2020-10-29 00:47:23 +01:00
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<img src="https://user-images.githubusercontent.com/952378/63592437-e433e400-c5b1-11e9-9c2d-889c6e93d748.jpg" width="190"><img src="https://user-images.githubusercontent.com/952378/63592435-e433e400-c5b1-11e9-88e4-3e441b61fa67.jpg" width="190"><img src="https://user-images.githubusercontent.com/952378/63592440-e4cc7a80-c5b1-11e9-8964-2cd1b22c87be.jpg" width="230"><img src="https://user-images.githubusercontent.com/952378/63592438-e4cc7a80-c5b1-11e9-86dc-a9e9f8555422.jpg" width="230">
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2020-01-15 19:49:18 +01:00
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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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2019-12-10 13:00:05 +01:00
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2020-01-15 19:49:18 +01:00
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## Installation
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2020-10-23 19:26:51 +02:00
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Clone the repository, enter it and run
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`pip install .`
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2020-01-16 15:56:32 +01:00
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### Models
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2020-09-16 22:10:47 +02:00
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Pre-trained models can be downloaded from here:
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2020-01-16 15:47:52 +01:00
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https://qurator-data.de/sbb_binarization/
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2020-10-23 19:26:51 +02:00
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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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