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Building efficient CNN architecture for offline handwritten Chinese character recognition

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Abstract

Deep convolutional neural networks-based methods have brought great breakthrough in image classification, which provides an end-to-end solution for handwritten Chinese character recognition (HCCR) problem through learning discriminative features automatically. Nevertheless, state-of-the-art CNNs appear to incur huge computational cost and require the storage of a large number of parameters especially in fully connected layers, which is difficult to deploy such networks into alternative hardware devices with limited computation capacity. To solve the storage problem, we propose a novel technique called weighted average pooling for reducing the parameters in fully connected layer without loss in accuracy. Besides, we implement a cascaded model in single CNN by adding mid output to complete recognition as early as possible, which reduces average inference time significantly. Experiments are performed on the ICDAR-2013 offline HCCR dataset. It is found that our proposed approach only needs 6.9 ms for classifying a character image on average and achieves the state-of-the-art accuracy of 97.1% while requires only 3.3 MB for storage.

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Acknowledgements

This paper is supported by Beijing Technology Plan Project: Z171100002217094 and National Defense Science and Technology Project: 17-163-12-XJ-003-003-01.

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Authors and Affiliations

  1. Lab of High-speed Circuit and Neural Networks, Institute of Semiconductors, CAS, Beijing, China

    Zhiyuan Li, Nanjun Teng, Min Jin & Huaxiang Lu

  2. University of Chinese Academy of Sciences, Beijing, China

    Zhiyuan Li, Nanjun Teng, Min Jin & Huaxiang Lu

  3. Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing, China

    Zhiyuan Li, Nanjun Teng, Min Jin & Huaxiang Lu

  4. CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China

    Huaxiang Lu

Authors
  1. Zhiyuan Li
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  2. Nanjun Teng
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  3. Min Jin
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  4. Huaxiang Lu
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Correspondence to Min Jin.

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Li, Z., Teng, N., Jin, M. et al. Building efficient CNN architecture for offline handwritten Chinese character recognition. IJDAR 21, 233–240 (2018). https://doi.org/10.1007/s10032-018-0311-4

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  • DOI: https://doi.org/10.1007/s10032-018-0311-4

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