https://github.com/MauritsBleeker/Bi-STET

Implementation of Bidirectional Scene Text Recognition with a Single Decoder
https://github.com/MauritsBleeker/Bi-STET

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Implementation of Bidirectional Scene Text Recognition with a Single Decoder

• Host: GitHub
• URL: https://github.com/MauritsBleeker/Bi-STET
• Owner: MauritsBleeker
• Created: 2019-12-08T11:22:31.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-07-13T15:03:46.000Z (over 4 years ago)
• Last Synced: 2024-08-02T11:14:52.633Z (3 months ago)
• Language: Python
• Homepage: https://arxiv.org/abs/1912.03656
• Size: 78.1 KB
• Stars: 67
• Watchers: 9
• Forks: 15
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Bi-STET

This is the repository for 'Bidirectional Scene Text Recognition with a Single Decoder', by Maurits Bleeker and Maarten de Rijke [[pdf](https://arxiv.org/pdf/1912.03656.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 the annotation 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}

}

```