Document Image Binarization
Python 3.8-3.11
with Tensorflow <2.13
are currently supported. While newer versions might also work, we currently don't test this.
You can either install from PyPI via
pip install sbb-binarization
or clone the repository, enter it and install (editable) with
git clone [email protected]:qurator-spk/sbb_binarization.git
cd sbb_binarization; pip install -e .
Alternatively, download the prebuilt image from Dockerhub:
docker pull ocrd/sbb_binarization
Pre-trained models can be downloaded from the locations below. We also provide models and model cards on 🤗
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 |
With OCR-D, you can also use the Resource Manager, e.g.
ocrd resmgr download ocrd-sbb-binarize "*"
sbb_binarize \
-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.
sbb_binarize -m /path/to/model/ myimage.tif myimage-bin.tif
To use the OCR-D interface:
ocrd-sbb-binarize -I INPUT_FILE_GRP -O OCR-D-IMG-BIN -P model default
For simple smoke tests, the following will
-
download models
-
download test data
-
run the OCR-D wrapper (on page and region level):
make models make test
If you find this tool useful in your work, please consider citing our paper:
@inproceedings{hip23rezanezhad2,
author = {Vahid Rezanezhad and Konstantin Baierer and Clemens Neudecker},
editor = {Apostolos Antonacopoulos and Christian Clausner and Maud Ehrmann and Kai Labusch and Clemens Neudecker},
title = {A hybrid CNN-Transformer Model for Historical Document Image Binarization},
booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing {HIP} 2023,
San José, CA, USA, August 26, 2023},
year = {2023},
url = {https://doi.org/10.1145/3604951.3605508}
}