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Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology

  • Application of soft computing
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Abstract

To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively.

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Authors and Affiliations

  1. Department of Literature and Law, Zhengzhou Shengda University of Economics Business and Management, Zhengzhou, 451191, Henan, China

    Huihui Si

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  1. Huihui Si
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Si, H. Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology. Soft Comput 28, 721–736 (2024). https://doi.org/10.1007/s00500-023-09423-y

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