PyTorch Project for NeurIPS 2018 paper:
Connectionist Temporal Classification with Maximum Entropy Regularization Hu Liu, Sheng Jin and Changshui Zhang. Neural Information Processing Systems (NeurIPS), 2018.
Connectionist Temporal Classification (CTC) is an objective function for end-to-end sequence learning, which adopts dynamic programming algorithms to directly learn the mapping between sequences. CTC has shown promising results in many sequence learning applications including speech recognition and scene text recognition. However, CTC tends to produce highly peaky and overconfident distributions, which is a symptom of overfitting. To remedy this, we propose a regularization method based on maximum conditional entropy which penalizes peaky distributions and encourages exploration. We also introduce an entropy-based pruning method to dramatically reduce the number of CTC feasible paths by ruling out unreasonable alignments. Experiments on scene text recognition show that our proposed methods consistently improve over the CTC baseline without the need to adjust training settings. Code has been made publicly available at: https://github.com/liuhu-bigeye/enctc.crnn.
zsh shs/seg_ent_fb/seg_5k.sh
CUDA_VISIBLE_DEVICES=0 python test.py --crnn_path model_dir --valroot data/svt1/testset.lmdb
If you use this code in your project, please consider citing this paper.
@inproceedings{liu2018connectionist,
title={Connectionist Temporal Classification with Maximum Entropy Regularization},
author={Liu, Hu and Jin, Sheng and Zhang, Changshui},
booktitle={Advances in Neural Information Processing Systems},
pages = {837--847},
year={2018}
}To ask questions or report issues, please open an issue on the issues tracker.
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CRNN pytorch implementation
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