Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/lquirosd/P2PaLA
Page to PAGE Layout Analysis Tool
https://github.com/lquirosd/P2PaLA
computer-vision deep-neural-networks document-layout-analysis gan generative-adversarial-network handwritten-text-recognition image-segmentation page-xml pix2pix pytorch
Last synced: about 2 months ago
JSON representation
Page to PAGE Layout Analysis Tool
- Host: GitHub
- URL: https://github.com/lquirosd/P2PaLA
- Owner: lquirosd
- License: gpl-3.0
- Archived: true
- Created: 2017-11-28T13:58:06.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-01-17T15:26:54.000Z (almost 3 years ago)
- Last Synced: 2024-08-02T11:14:26.414Z (5 months ago)
- Topics: computer-vision, deep-neural-networks, document-layout-analysis, gan, generative-adversarial-network, handwritten-text-recognition, image-segmentation, page-xml, pix2pix, pytorch
- Language: Python
- Size: 849 KB
- Stars: 191
- Watchers: 14
- Forks: 42
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
P2PaLA
======
:exclamation::exclamation: P2PaLA is deprecated :exclamation::exclamation:
[](https://www.python.org/)
[](https://github.com/ambv/black)
Page to [PAGE](http://www.primaresearch.org/tools/PAGELibraries) Layout Analysis (P2PaLA) is a toolkit for Document Layout Analysis based on Neural Networks.
:boom: Try our new [DEMO](http://prhlt-carabela.prhlt.upv.es/tld/) for online baseline detection. :exclamation::exclamation:
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}},
}
```
Check this paper for more details [Arxiv](https://arxiv.org/abs/1806.08852).
Requirements
===========
- Linux (OSX may work, but untested.).
- [Python](https://www.python.org/) (2.7, 3.6 under [conda virtual environment](https://www.anaconda.com/download/#linux) is recomended)
- [Numpy](http://www.numpy.org/)
- [PyTorch](http://pytorch.org) (1.0). PyTorch 0.3.1 compatible on this [branch](https://github.com/lquirosd/P2PaLA/tree/PyTorch-v0.3.1)
- [OpenCv](https://github.com/opencv/opencv/releases/tag/3.4.5) (3.4.5.20).
- NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN works, but is not recomended for training).
- [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch) (v0.9) [Optional]. `pip install tensorboardX` > A diferent conda env is recomended to keep tensorflow separated from PyTorch
Install
=======
```bash
python setup.py install
```
> To install python dependencies alone, use [requirements file](conda_requirements.yml) `conda env create --file conda_requirements.yml`
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:
```bash
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.
```bash
python P2PaLA.py --config config.txt --tr_data ./data/train --te_data ./data/test --log_comment "_foo"
```
> :exclamation: Pre-trained models available [here](egs/pre_trained)
3. Use TensorBoard to visualize train status:
```bash
tensorboard --logdir ./work/runs
```
4. xml-PAGE files must be at "./work/results/test/"
> We recommend [Transkribus](https://transkribus.eu/Transkribus/) or [nw-page-editor](https://github.com/mauvilsa/nw-page-editor)
> to visualize and edit PAGE-xml files.
5. For detail about arguments and config file, see [docs](docs) or `python P2PaLA.py -h`.
6. For more detailed example see [egs](egs):
* Bozen dataset [see](egs/Bozen)
* cBAD complex competition dataset [see](egs/cBAD_complex)
* OHG dataset [see](egs/OHG)
License
=======
GNU General Public License v3.0
See [LICENSE](LICENSE) to see the full text.
Acknowledgments
===============
Code is inspired by [pix2pix](https://github.com/phillipi/pix2pix) and [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)