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Recognition method for stone carved calligraphy characters based on a convolutional neural network

  • S.i.: Ai Based Techniques and Applications for Intelligent Iot Systems
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Abstract

Chinese calligraphy is an important part of Chinese national culture and art and part of the essence of Chinese national culture. Stone calligraphy is one of the important elements of Chinese calligraphy art. Stone carved calligraphy characters have high cultural and artistic value. Therefore, accurately recognizing stone carved calligraphy characters are of great importance. Stone carved calligraphy can identify hard-to-preserve stone calligraphy paper materials in electronic data that can be preserved for a long time, thereby offering important reference materials for the study of the historical development of Chinese calligraphy art. Moreover, with the development of science and technology and the investment of China in cultural and artistic undertakings, computer-aided calligraphy character recognition technology is also constantly improving, and its application in calligraphy recognition is becoming increasingly extensive. This article aims to study a method of stone inscription calligraphy recognition based on convolutional neural networks. In this paper, we use an image recognition and optimization method consisting of a convolutional neural network to carry out an experiment with stone inscription calligraphy characters. It was concluded that the recognition accuracy of stone calligraphy characters by the convolutional neural network reached 99.2%, indicating that this stone calligraphy character recognition method based on a convolutional neural network has a good ability to recognize stone calligraphy characters.

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Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Glorious Sun School of Business and Management, Donghua University, Shanghai, 200051, China

    Ji-dan Huang

  2. Garces Memorial High School, 2800 Loma Linda Drive, Bakersfield, CA, USA

    Guanjie Cheng

  3. Bakersfield College, 1801 Panorama Drive, Bakersfield, CA, USA

    Jinghan Zhang

  4. Guangxi Vocational College of Performing Arts, Nanning, 530000, China

    Wei Miao

Authors
  1. Ji-dan Huang
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  2. Guanjie Cheng
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  3. Jinghan Zhang
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  4. Wei Miao
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Correspondence to Wei Miao.

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Ji-dan Huang, Guanjie Cheng, Jinghan Zhang are co-first author.

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Huang, Jd., Cheng, G., Zhang, J. et al. Recognition method for stone carved calligraphy characters based on a convolutional neural network. Neural Comput & Applic 35, 8723–8732 (2023). https://doi.org/10.1007/s00521-022-08049-9

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  • DOI: https://doi.org/10.1007/s00521-022-08049-9

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