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Writer adaptation via deeply learned features for online Chinese handwriting recognition

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Abstract

This paper proposes a novel framework of writer adaptation based on deeply learned features for online handwritten Chinese character recognition. Our motivation is to further boost the state-of-the-art deep learning-based recognizer by using writer adaptation techniques. First, to perform an effective and flexible writer adaptation, we propose a tandem architecture design for the feature extraction and classification. Specifically, a deep neural network (DNN) or convolutional neural network (CNN) is adopted to extract the deeply learned features which are used to build a discriminatively trained prototype-based classifier initialized by Linde–Buzo–Gray clustering techniques. In this way, the feature extractor can fully utilize the useful information of a DNN or CNN. Meanwhile, the prototype-based classifier could be designed more compact and efficient as a practical solution. Second, the writer adaption is performed via a linear transformation of the deeply learned features which is optimized with a sample separation margin-based minimum classification error criterion. Furthermore, we improve the generalization capability of the previously proposed discriminative linear regression approach for writer adaptation by using the linear interpolation of two transformations and adaptation data perturbation. The experiments on the tasks of both the CASIA-OLHWDB benchmark and an in-house corpus with a vocabulary of 20,936 characters demonstrate the effectiveness of our proposed approach.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61671422.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, Anhui, People’s Republic of China

    Jun Du & Jian-Fang Zhai

  2. iFlytek Research, Hefei, Anhui, People’s Republic of China

    Jin-Shui Hu

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  1. Jun Du
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  2. Jian-Fang Zhai
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  3. Jin-Shui Hu
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Correspondence to Jun Du.

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Du, J., Zhai, JF. & Hu, JS. Writer adaptation via deeply learned features for online Chinese handwriting recognition. IJDAR 20, 69–78 (2017). https://doi.org/10.1007/s10032-017-0280-z

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  • DOI: https://doi.org/10.1007/s10032-017-0280-z

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