This is an UNOFFICIAL repo for the SauvolaNet (ICDAR2021).
For the Original repo visit the following URL
Documentation of SauvolaNet-Training can be found here
My Main Contribution are in the following:
- Training. Added training functions for both pretrained and from scratch model.
- Configuration file to customize nearly everything in the pipeline.
- Wandb API Support (Just add WandbCallback in the callbacks in Config file).
- Image Augmentations
SauvolaNet is an end-to-end document binarization solution. It is optimal for three hyper-parameters of the classic Sauvola algorithm. Compare with existing solutions, SauvolaNet has followed advantages:
- SauvolaNet do not have any Pre/Post-processing
- SauvolaNet has comparable performance with SoTA
- SauvolaNet has a super lightweight network structure and faster than DNN-based SoTA
More precisely, SauvolaNet consists of three modules, namely, Multi-window Sauvola (MWS), Pixelwise Window Attention (PWA), and Adaptive Sauolva Threshold (AST).
- MWS generates multiple windows of different size Sauvola with trainable parameters
- PWA generates pixelwise attention of window size
- AST generates pixelwise threshold by fusing the result of MWS and PWA.
LineCounter is written in TensorFlow.
- TensorFlow-GPU: 1.15.0
- keras-gpu 2.2.4
Other versions might also work but are not tested.
Download the repo and create the virtual environment by following commands
conda create --name LineCounter --file spec-env.txt
conda activate Sauvola
pip install tensorflow-gpu==1.15.0
pip install opencv-python
pip install parse
Then play with the provided notebook.
Alternatively, one may play with the inference code using this google colab link.
Edit Config.yaml file and simply run the following to start training:
(Check the following for more details on Config parameters)
$ python train.pyCan even edit some configurations directly from the command line.
The following list can be viewed through --help
$ python train.py -h
usage: train.py [-h] [-c CONF] [-a ARGS [ARGS ...]]
optional arguments:
-h, --help show this help message and exit
-c CONF, --conf CONF configuration file path
-a ARGS [ARGS ...], --args ARGS [ARGS ...]
configuration arguments. e.g.: -a Train.loss=mseFor each image there should be an image for the original image e.g. TRAIN_image1_source.jpg, and an image for the ground truth image e.g. TRAIN_image1_target.jpg
The pattern to match source and groundtruth images is the name before the '_source.*' or '_target.*'
Besides, all the names should begin with 'TRAIN_'
| ImageID | Src Image: TRAIN_<uniqueID>_source.<Extention> |
Target Image: TRAIN_<uniqueID>_target.<Extention> |
|---|---|---|
| P03 | Dataset/TRAIN_P03_source.png |
Dataset/TRAIN_P03_source.png |
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We do not own the copyright of the dataset used in this repo.
Below is a summary table of the datasets used in this work along with a link from which they can be downloaded:
| Dataset | URL |
|---|---|
| DIBCO 2009 | http://users.iit.demokritos.gr/~bgat/DIBCO2009/benchmark/ |
| DIBCO 2010 | http://users.iit.demokritos.gr/~bgat/H-DIBCO2010/benchmark/ |
| DIBCO 2011 | http://utopia.duth.gr/~ipratika/DIBCO2011/benchmark/ |
| DIBCO 2012 | http://utopia.duth.gr/~ipratika/HDIBCO2012/benchmark/ |
| DIBCO 2013 | http://utopia.duth.gr/~ipratika/DIBCO2013/benchmark/ |
| DIBCO 2014 | http://users.iit.demokritos.gr/~bgat/HDIBCO2014/benchmark/ |
| DIBCO 2016 | http://vc.ee.duth.gr/h-dibco2016/benchmark/ |
| DIBCO 2017 | https://vc.ee.duth.gr/dibco2017/ |
| DIBCO 2018 | https://vc.ee.duth.gr/h-dibco2018/ |
| PHIDB | http://www.iapr-tc11.org/mediawiki/index.php/Persian_Heritage_Image_Binarization_Dataset_(PHIBD_2012) |
| Bickely-diary dataset | https://www.comp.nus.edu.sg/~brown/BinarizationShop/dataset.htm |
| Synchromedia Multispectral dataset | http://tc11.cvc.uab.es/datasets/SMADI_1 |
| Monk Cuper Set | https://www.ai.rug.nl/~sheng/ |
For any paper-related questions, contact the original authors
For any code-related questions, feel free to contact me in the issues section :)