Bi-STET

This is the repository for 'Bidirectional Scene Text Recognition with a Single Decoder', by Maurits Bleeker and Maarten de Rijke [pdf]

The base source-code for this project comes from: http://nlp.seas.harvard.edu/2018/04/03/attention.html

I have tried to keep the code as general as possible. However, some elements of the pipeline are specially for the environment I worked with.

Model weights and reproducibility

To reproduce the results of the paper, please use the final model parameters.

https://drive.google.com/file/d/1OwJ3iVpRhnjIZyOi7aOQIeLv7N1DHZkC/view?usp=sharing

In the folder data_utils/, all the scripts to generate the train and test sets as used for this paper are provided.

Python and package versions

Python 3.7
Pillow 5.4.1
nltk 3.4.5
numpy 1.17.1
scipy 1.2.0
seaborn 0.9.0
tensorboard-logger 0.1.0
tensorboardX 1.7
torch 1.1.0.post2
torchvision 0.2.1
transformers 2.1.1

Run

To run the code, just run main.py, and set all the configurations in the Config.py. The configurations to reproduce the results are set in the Config.py file.

Training

There are two options to load the training/test data:

From disk. This can be done by using the annotation file(s).
From a pickle file. The pickle file should contain a python dict with the following data format.

{
image_id : { 
    'data' : 'binary image string',
    'label' : 'word'
    }
}

Test and train annotations

The annotations text files are formatted as 'path/to/image.jpg annotation'. The path to image is always relative to a root folder.

Example root folder: User/Documents/Project/data/IIITK/

In User/Documents/Project/data/IIITK/, we have an annotation.txt and the images.

An example of annotation the file:

test/1002_1.png private

Data processing

All the files to process the original provided train datasets are given in /data_utils.

Reference

If you found this code useful, please cite the following paper:

@article{bleeker2019bidirectional,
  title={Bidirectional Scene Text Recognition with a Single Decoder},
  author={Bleeker, Maurits and de Rijke, Maarten},
  journal={arXiv preprint arXiv:1912.03656},
  year={2019}
}

ustczhouyu / Bi-STET