SqueezedText: A Real-Time Scene Text Recognition by Binary Convolutional Encoder-Decoder Network

@inproceedings{Liu2018SqueezedTextAR,
  title={SqueezedText: A Real-Time Scene Text Recognition by Binary Convolutional Encoder-Decoder Network},
  author={Zichuan Liu and Yixing Li and Fengbo Ren and Wang Ling Goh and Hao Yu},
  booktitle={AAAI},
  year={2018}
}
A new approach for real-time scene text recognition is proposed in this paper. A novel binary convolutional encoderdecoder network (B-CEDNet) together with a bidirectional recurrent neural network (Bi-RNN). The B-CEDNet is engaged as a visual front-end to provide elaborated character detection, and a back-end Bi-RNN performs characterlevel sequential correction and classification based on learned contextual knowledge. The front-end B-CEDNet can process multiple regions containing characters… CONTINUE READING

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Key Quantitative Results

  • By training with over 1,000,000 synthetic scene text images, the B-CEDNet achieves a recall rate of 0.86, precision of 0.88 and F-score of 0.87 on ICDAR-03 and ICDAR-13.
  • By training with over 1,000,000 synthetic scene text images, the proposed SqueezedText can achieve recall rate of 0.86, precision of 0.88 and F-score of 0.87 on ICDAR-03 (Lucas et al.

References

Publications referenced by this paper.
Showing 1-10 of 37 references

An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2017
View 11 Excerpts
Highly Influenced

End-to-end sequence labeling via bi-directional lstm-cnns-crf

Ma, X. Hovy 2016 Ma, E. Hovy
arXiv preprint arXiv:1603.01354 • 2016

Recursive Recurrent Nets with Attention Modeling for OCR in the Wild

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2016