Edit Probability for Scene Text Recognition

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Abstract

We consider the scene text recognition problem under the attention-based encoder-decoder framework, which is the state of the art. The existing methods usually employ a frame-wise maximal likelihood loss to optimize the models. When we train the model, the misalignment between the ground truth strings and the attention’s output sequences of probability distribution, which is caused by missing or superfluous characters, will confuse and mislead the training process, and consequently make the training costly and degrade the recognition accuracy. To handle this problem, we propose a novel method called edit probability (EP) for scene text recognition. EP tries to effectively estimate the probability of generating a string from the output sequence of probability distribution conditioned on the input image, while considering the possible occurrences of missing/superfluous characters. The advantage lies in that the training process can focus on the missing, superfluous and unrecognized characters, and thus the impact of the misalignment problem can be alleviated or even overcome. We conduct extensive experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets. Experimental results show that the EP can substantially boost scene text recognition performance. [Paper]

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Highlights Contributions

❃ we propose a novel method called edit probability (EP) under the attention-based encoder-decoder framework for scene text recognition, to reduce the impact of the misalignments caused by missing or superfluous characters.

❃ We conduct extensive experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets. Experimental results show that the EP can substantially boost scene text recognition performance.

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Recommended Citations

If you find our work is helpful to your research, please feel free to cite us:

@inproceedings{bai2018edit,
    title={Edit Probability for Scene Text Recognition},
    author={Bai, Fan and Cheng, Zhanzhan and Niu, Yi and Pu, Shiliang and Zhou, Shuigeng},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={1508--1516},
    year={2018},
}