Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/qurator-spk/sbb_binarization
Document Image Binarization
https://github.com/qurator-spk/sbb_binarization
binarization ocr qurator
Last synced: 14 days ago
JSON representation
Document Image Binarization
- Host: GitHub
- URL: https://github.com/qurator-spk/sbb_binarization
- Owner: qurator-spk
- License: apache-2.0
- Created: 2019-12-10T11:49:01.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-17T14:09:42.000Z (22 days ago)
- Last Synced: 2024-10-19T20:21:12.902Z (20 days ago)
- Topics: binarization, ocr, qurator
- Language: Python
- Homepage:
- Size: 159 KB
- Stars: 73
- Watchers: 6
- Forks: 15
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- awesome-lists - sbb binarization
README
# sbb_binarization
> Document Image Binarization
[](https://pypi.org/project/sbb-binarization/)
[](https://github.com/qurator-spk/sbb_binarization/actions/workflows/test.yml)
[](https://github.com/qurator-spk/sbb_binarization/actions/workflows/docker-image.yml)
## Installation
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
### Models
Pre-trained models can be downloaded from the locations below. We also provide models and [model cards](https://huggingface.co/SBB/sbb_binarization) 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](https://ocr-d.de/), you can also use the [Resource Manager](https://ocr-d.de/en/models), e.g.
ocrd resmgr download ocrd-sbb-binarize "*"
## Usage
```sh
sbb_binarize \
-m \
\
```
**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
sbb_binarize -m /path/to/model/ myimage.tif myimage-bin.tif
To use the [OCR-D](https://ocr-d.de/en/spec/cli) interface:
ocrd-sbb-binarize -I INPUT_FILE_GRP -O OCR-D-IMG-BIN -P model default
## Testing
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
## How to cite
If you find this tool useful in your work, please consider citing our paper:
```bibtex
@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}
}
```