qurator-spk/sbb_binarization
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# Binarization
# sbb_binarization
> Binarization for document images
> Document Image Binarization using pre-trained models
[![pip release](https://img.shields.io/pypi/v/sbb-binarization.svg)](https://pypi.org/project/sbb-binarization/)
[![CircleCI test](https://circleci.com/gh/qurator-spk/sbb_binarization.svg?style=shield)](https://circleci.com/gh/qurator-spk/sbb_binarization)
@ -10,28 +10,35 @@
<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 using a trained ResNet50-UNet model.
## Installation
Clone the repository, enter it and run
Python versions `3.7-3.10` are currently supported.
`pip install .`
You can either install via
```
pip install sbb-binarization
```
or clone the repository, enter it and install (editable) with
```
git clone git@github.com:qurator-spk/sbb_binarization.git
cd sbb_binarization; pip install -e .
```
### Models
Pre-trained models in HDF5 format can be downloaded from here:
Pre-trained models can be downloaded from the locations below. We also provide the models and [model card](https://huggingface.co/SBB/sbb_binarization) on 🤗
https://qurator-data.de/sbb_binarization/
| Version | Format | Download |
|----------|:-------------:|-------|
| 2021-03-09 | `SavedModel` | https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2021_03_09.zip |
| 2021-03-09 | `HDF5` | https://qurator-data.de/sbb_binarization/2021-03-09/models.tar.gz |
| 2020-01-16 | `SavedModel` | https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2020_01_16.zip |
| 2020-01-16 | `HDF5` | https://qurator-data.de/sbb_binarization/2020-01-16/models.tar.gz |
We also provide models in Tensorflow SavedModel format via Huggingface and Github release assets:
https://huggingface.co/SBB/sbb_binarization
https://github.com/qurator-spk/sbb_binarization/releases
With [OCR-D](https://ocr-d.de/), you can use the [Resource Manager](Tensorflow SavedModel) to deploy models, e.g.
With [OCR-D](https://ocr-d.de/), you can use the [Resource Manager](https://ocr-d.de/en/models) to deploy models, e.g.
ocrd resmgr download ocrd-sbb-binarize "*"
@ -40,11 +47,13 @@ With [OCR-D](https://ocr-d.de/), you can use the [Resource Manager](Tensorflow S
```sh
sbb_binarize \
-m <path to directory containing model files \
-m <path to directory containing model files> \
<input image> \
<output image>
```
**Note:** the output image MUST use either `.tif` or `.png` as file extension to produce a binary image. Input images can also be JPEG.
Images containing a lot of border noise (black pixels) should be cropped beforehand to improve the quality of results.
### Example
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- download test data
- run the OCR-D wrapper (on page and region level):
make model
make models
make test