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ColDBin: Cold Diffusion for Document Image Binarization
This repository contains the datasets and code for the paper ColDBin: Cold Diffusion for Document Image Binarization by Saifullah Saifullah, Stefan Agne, Andreas Dengel, and Sheraz Ahmed.
Requires Python 3+. For evaluation, please download the data from the links below.
Approach:
<img align="center" src="assets/approach.png">Qualitative Results:
<img align="center" src="assets/qualitative.png">Quantitative Results
| Dataset | FM | p-FM | PSNR | DRD |
|---|---|---|---|---|
| DIBCO 2009 | 94.19 | 96.52 | 20.65 | 2.58 |
| DIBCO 2010 | 95.29 | 96.67 | 22.06 | 1.36 |
| DIBCO 2011 | 95.23 | 96.93 | 21.53 | 1.44 |
| DIBCO 2012 | 96.37 | 97.41 | 23.40 | 1.28 |
| DIBCO 2013 | 96.62 | 97.15 | 23.98 | 1.20 |
| DIBCO 2014 | 97.89 | 98.10 | 24.38 | 0.66 |
| DIBCO 2016 | 89.50 | 93.73 | 18.71 | 3.84 |
| DIBCO 2017 | 93.04 | 95.12 | 19.32 | 2.29 |
| DIBCO 2018 | 89.71 | 93.00 | 19.53 | 3.82 |
Prepare dibco datasets
Download the datasets from the link: Use the example dataset preparation script provided for DIBCO 2013 dataset:
./scripts/prepare_dataset.sh
Train
Train a diffusion model in cold manner using the example training script for DIBCO 2013 dataset:
./scripts/train.sh
Test:
Test the trained model using the example testing script for DIBCO 2013 dataset:
./scripts/test.sh
License
This repository is released under the Apache 2.0 license as found in the LICENSE file.