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Applying Deep Convolutional Neural Network to Cursive Chinese Calligraphy Recognition

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New Trends in Computer Technologies and Applications (ICS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1013))

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Abstract

Calligraphy is one of the most important cultural art as well as writing tool in ancient China. Various writing styles have evolved over time in Calligraphy text, including Regular script, Clerical script, Semi-cursive script, Cursive script, and Seal script. In this study, we consider the cursive Chinese calligraphy recognition task as a variant of handwritten text recognition. We apply deep convolutional network approach to this recognition problem and achieve 84.6%, 92.6%, 93.7%, 96.7% average top1, top3, top5, top10 accuracy for 395 characters and 83.8%, 91.8%, 94%, 96.1% average top1, top3, top5, top10 accuracy for 632 characters. Our investigation indicates that text recognition tasks can be tackled by deep learning based approach even only when a limited number of training samples are available.

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Author information

Authors and Affiliations

  1. Department of Computer Science, National Chengchi University, Taipei, Taiwan

    Liang Jung & Wen-Hung Liao

Authors
  1. Liang Jung
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  2. Wen-Hung Liao
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Corresponding author

Correspondence to Wen-Hung Liao .

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Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chuan-Yu Chang

  2. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chien-Chou Lin

  3. Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Horng-Horng Lin

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Jung, L., Liao, WH. (2019). Applying Deep Convolutional Neural Network to Cursive Chinese Calligraphy Recognition. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_71

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  • DOI: https://doi.org/10.1007/978-981-13-9190-3_71

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9189-7

  • Online ISBN: 978-981-13-9190-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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