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95 lines
4.0 KiB
Markdown
95 lines
4.0 KiB
Markdown
# sbb_binarization
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> Document Image Binarization
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[](https://pypi.org/project/sbb-binarization/)
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[](https://github.com/qurator-spk/sbb_binarization/actions/workflows/test.yml)
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[](https://github.com/qurator-spk/sbb_binarization/actions/workflows/docker-image.yml)
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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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## Installation
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Python `3.8-3.11` with Tensorflow `<2.13` are currently supported. While newer versions might also work, we currently don't test this.
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You can either install from PyPI via
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pip install sbb-binarization
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or clone the repository, enter it and install (editable) with
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git clone git@github.com:qurator-spk/sbb_binarization.git
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cd sbb_binarization; pip install -e .
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Alternatively, download the prebuilt image from Dockerhub:
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docker pull ocrd/sbb_binarization
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### Models
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Pre-trained models can be downloaded from the locations below. We also provide models and [model cards](https://huggingface.co/SBB/sbb_binarization) on 🤗
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| Version | Format | Download |
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|------------|:-------------:|------------------------------------------------------------------------------------------------------|
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| 2021-03-09 | `SavedModel` | https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2021_03_09.zip |
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| 2021-03-09 | `HDF5` | https://qurator-data.de/sbb_binarization/2021-03-09/models.tar.gz |
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| 2020-01-16 | `SavedModel` | https://github.com/qurator-spk/sbb_binarization/releases/download/v0.0.11/saved_model_2020_01_16.zip |
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| 2020-01-16 | `HDF5` | https://qurator-data.de/sbb_binarization/2020-01-16/models.tar.gz |
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With [OCR-D](https://ocr-d.de/), you can also use the [Resource Manager](https://ocr-d.de/en/models), e.g.
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ocrd resmgr download ocrd-sbb-binarize "*"
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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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**Note:** the output image MUST use either `.tif` or `.png` as file extension to produce a binary image. Input images can also be JPEG.
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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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sbb_binarize -m /path/to/model/ myimage.tif myimage-bin.tif
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To use the [OCR-D](https://ocr-d.de/en/spec/cli) interface:
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ocrd-sbb-binarize -I INPUT_FILE_GRP -O OCR-D-IMG-BIN -P model default
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## Testing
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For simple smoke tests, the following will
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- download models
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- download test data
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- run the OCR-D wrapper (on page and region level):
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make models
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make test
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## How to cite
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If you find this tool useful in your work, please consider citing our paper:
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```bibtex
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@inproceedings{hip23rezanezhad2,
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author = {Vahid Rezanezhad and Konstantin Baierer and Clemens Neudecker},
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editor = {Apostolos Antonacopoulos and Christian Clausner and Maud Ehrmann and Kai Labusch and Clemens Neudecker},
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title = {A hybrid CNN-Transformer Model for Historical Document Image Binarization},
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booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing {HIP} 2023,
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San José, CA, USA, August 26, 2023},
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year = {2023},
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url = {https://doi.org/10.1145/3604951.3605508}
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}
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```
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