Abstract
Text recognition has attracted much attention and achieved exciting results on several commonly used public English datasets in recent years. However, most of these well-established methods, such as connectionist temporal classification (CTC)-based methods and attention-based methods, pay less attention to challenges on the Chinese scene, especially for long text sequences. In this paper, we exploit the characteristic of Chinese word frequency distribution and propose a hybrid CTC-Attention decoder (HCADecoder) supervised with bigram mixture labels for Chinese text recognition. Specifically, we first add high-frequency bigram subwords into the original unigram labels to construct the mixture bigram label, which can shorten the decoding length. Then, in the decoding stage, the CTC module outputs a preliminary result, in which confused predictions are replaced with bigram subwords. The attention module utilizes the preliminary result and outputs the final result. Experimental results on four Chinese datasets demonstrate the effectiveness of the proposed method for Chinese text recognition, especially for long texts. Code will be made publicly available(https://github.com/lukecsq/hybrid-CTC-Attention).
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Notes
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One- and two-character words are defined by the Chinese semantics and have the same length as unigram and bigram words, respectively.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant U2034211, 62006017, in part by the Fundamental Research Funds for the Central Universities under Grant 2020JBZD010 and in part by the Beijing Natural Science Foundation under Grant L191016.
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Cai, S., Xue, W., Li, Q., Zhao, P. (2021). HCADecoder: A Hybrid CTC-Attention Decoder for Chinese Text Recognition. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_12
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