fendaq / p2pala

Page to PAGE Layout Analysis Tool

P2PaLA

Page to PAGE Layout Analysis (P2PaLA) is a toolkit for Document Layout Analysis based on Neural Networks.

If you find this toolkit useful in your research, please cite:

@misc{p2pala2017,
  author = {Lorenzo Quirós},
  title = {P2PaLA: Page to PAGE Layout Analysis tookit},
  year = {2017},
  publisher = {GitHub},
  note = {GitHub repository},
  howpublished = {\url{https://github.com/lquirosd/P2PaLA}},
}

Or check this paper for details Arxiv.

Requirements

• Linux (OSX may work, but untested.).
• Python (2.7 under conda virtual environment is recomended)
• Python future pip install future
• Numpy (installed by default using conda)
• PyTorch (0.3.0). conda install pytorch torchvision -c pytorch
• OpenCv (3.1.0). conda install -c menpo opencv
• NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested).
• tensorboard-pytorch (v0.9) [Optional]. pip install tensorboardX > A diferent conda env is recomended to keep tensorflow separated from PyTorch

For a full list of dependencies see conda env file

Usage

1. Input data must follow the folder structure data_tag/page, where images must be into the data_tag folder and xml files into page. For example:

mkdir -p data/{train,val,test,prod}/page;
tree data;

data
├── prod
│   ├── page
│   │   ├── prod_0.xml
│   │   └── prod_1.xml
│   ├── prod_0.jpg
│   └── prod_1.jpg
├── test
│   ├── page
│   │   ├── test_0.xml
│   │   └── test_1.xml
│   ├── test_0.jpg
│   └── test_1.jpg
├── train
│   ├── page
│   │   ├── train_0.xml
│   │   └── train_1.xml
│   ├── train_0.jpg
│   └── train_1.jpg
└── val
    ├── page
    │   ├── val_0.xml
    │   └── val_1.xml
    ├── val_0.jpg
    └── val_1.jpg

2. Run the tool.

python P2PaLA.py --config config.txt --tr_data ./data/train --te_data ./data/test --log_comment "_foo"

3. Use TensorBoard to visualize train status:

tensorboard --logdir ./work/runs

4. xml-PAGE files must be at "./work/results/test/"

We recommend Transkribus or nw-page-editor to visualize and edit PAGE-xml files.

5. For detail about arguments and config file, see docs or python P2PaLa.py -h.
6. For more detailed example see egs:
   • Bozen dataset see
   • cBAD complex competition dataset see
   • OHG dataset see

License

GNU General Public License v3.0 See LICENSE to see the full text.

Acknowledgments

Code is inspired by pix2pix and pytorch-CycleGAN-and-pix2pix

To-do

• Save best model under criteria [best train L1, best val L1, ...]
• stop training after X epochs without improvement
• Provide an example of use
• Provide Docker
• Include BaselinePage to detect baselines.
• Test on Mac/OS.