Pixelwise binarization with selectional auto-encoders in Keras
Project description
Binarization
Binarization for document images
Examples
Introduction
This tool performs document image binarization using a trained ResNet50-UNet model.
Installation
Clone the repository, enter it and run
pip install .
Models
Pre-trained models in HDF5
format can be downloaded from here:
https://qurator-data.de/sbb_binarization/
We also provide a Tensorflow saved_model
via Huggingface:
https://huggingface.co/SBB/sbb_binarization
Usage
sbb_binarize \
-m <path to directory containing model files \
<input image> \
<output image>
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 interface:
ocrd-sbb-binarize --overwrite -I INPUT_FILE_GRP -O OCR-D-IMG-BIN -P model "/var/lib/sbb_binarization"
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sbb_binarization-0.0.11.tar.gz
(10.9 kB
view hashes)
Built Distribution
Close
Hashes for sbb_binarization-0.0.11-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a6b81da7b1f73cbedd6fa9f9a51b3005ed869a3d82f9cc2a13cccf96d54af71e |
|
MD5 | af49088fda20cd0f7e28c119112088e0 |
|
BLAKE2b-256 | 477863e6ff02129c7803d751fdf94022a8f0604277eabc56de6804b90a92485d |