You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
Go to file
Konstantin Baierer 30a3c98762 require h5py < 3, qurator-spk/sbb_textline_detection#50, tensorflow/tensorflow#44467 4 years ago
.circleci minimal CI setup 4 years ago
repo add assets subrepo 4 years ago
sbb_binarize 📦 v0.0.5 4 years ago
.gitignore 📦 v0.0.2 4 years ago
.gitkeep Add new directory, you can find corresponding models in qurator-data 5 years ago
.gitmodules add assets subrepo 4 years ago
CHANGELOG.md 📦 v0.0.5 4 years ago
LICENSE Add LICENSE 5 years ago
Makefile minimal CI setup 4 years ago
README.md Update README.md 4 years ago
make.sh Add new file 5 years ago
ocrd-tool.json add ocrd-tool.json 4 years ago
requirements.txt require h5py < 3, qurator-spk/sbb_textline_detection#50, tensorflow/tensorflow#44467 4 years ago
setup.py minimal CI setup 4 years ago

README.md

Binarization

Binarization for document images

Examples

Introduction

This tool performs document image binarization (i.e. transform colour/grayscale to black-and-white pixels) for OCR using multiple trained models.

The method used is based on Calvo-Zaragoza/Gallego, 2018. A selectional auto-encoder approach for document image binarization.

Installation

Clone the repository, enter it and run

pip install .

Models

Pre-trained models can be downloaded from here:

https://qurator-data.de/sbb_binarization/

Usage

sbb_binarize \
  --patches \
  -m <directory with models> \
  <input image> \
  <output image>

Note In virtually all cases, the --patches flag will improve results.