Scene-Text-OCR

This repository contains code for Scene Text OCR following the famous CRNN model. The detection part is handled by EAST and the recognition is done by CRNN.

Detection

For the detection part, I use EAST detector => CODE HERE. I finetune the network on products data.

Detection Results

Recognition

For the recognition part, I have implemented the CRNN network in tensorflow and finetuned it on the data. The data can be downloaded from here => DOWNLOAD HERE.

CRNN Case

P.S. This is old code and is no longer maintained, works with specific tf and Python versions. You can check it in the code.

First you need to generate three splits: train.txt, valid.txt, test.txt. The file label_generate.py generates the labels from these three files. Once loaded, you can simply call train.py to train the model. The logs can also be viewed in tensorboard and trained models are saved.

CRNN Results

BDLSTM Case

P.S. This is old code and is no longer maintained, works with specific tf and Python versions. You can check it in the code.

In this case, the splits are provided and the dataset is used similarly. This is solely based on Bi-directional LSTMs + CTC layer as a loss. The results are not that good compared to CRNN specially in case of scene images. However, it works fairly well in case of cropped binarized words.

References

If you use any of the code, please consider citing the original papers.

CRNN Paper

@article{shi2016end,
  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={39},
  number={11},
  pages={2298--2304},
  year={2016},
  publisher={IEEE}
}

EAST Paper

@inproceedings{zhou2017east,
  title={East: an efficient and accurate scene text detector},
  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={5551--5560},
  year={2017}
}

Credits

The code used in this repository is heavily borrowed from these repositories.

kjanjua26/Scene-Text-OCR