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
vahidrezanezhad 128aa8436d
Update requirements.txt
12 months ago
.circleci use resmgr for model download 3 years ago
repo add assets subrepo 4 years ago
sbb_binarize enable flowing from directory 2 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 Update CHANGELOG.md 2 years ago
LICENSE Add LICENSE 4 years ago
Makefile fix test 3 years ago
README.md improve usage instructions 2 years ago
make.sh Add new file 5 years ago
ocrd-tool.json add ocrd-tool.json 4 years ago
requirements.txt Update requirements.txt 12 months ago
setup.py minimal CI setup 4 years ago

README.md

Binarization

Binarization for document images

Examples

Introduction

This tool performs document image binarization using trained models. The method is based on Calvo-Zaragoza and Gallego, 2018.

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 <path to directory containing model files> \
  <input image> \
  <output image>

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

Example

sbb_binarize --patches -m /path/to/models/ 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"