Volume 32 Issue 1
Jan.  2023
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TANG Minli, XIE Shaomin, LIU Xiangrong, “Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 64-75, 2023, doi: 10.23919/cje.2022.00.077
Citation: TANG Minli, XIE Shaomin, LIU Xiangrong, “Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 64-75, 2023, doi: 10.23919/cje.2022.00.077

Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

doi: 10.23919/cje.2022.00.077
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  • Author Bio:

    Minli TANG was born in Guizhou Province, China, in 1982. She is an Associate Professor at KaiLi University. She is currently studying for her Ph.D. degree at Xiamen University. Her research interests include computational intelligence, pattern recognition, and computer vision. (Email: tangml@stu.xmu.edu.cn)

    Shaomin XIE was born in Guangdong Province, China, in 1998. He is studying for a master’s degree at Xiamen University. His research interests include computational intelligence and pattern recognition. (Email: xsmin@stu.xmu.edu.cn)

    Xiangrong LIU (corresponding author) was born in Hunan Province, China, in 1978. He is a Professor and Ph.D. Supervisor at Xiamen University. His research interests include computational intelligence, data mining, and computational theory. (Email: xrliu@xmu.edu.cn)

  • Received Date: 2022-04-07
  • Accepted Date: 2022-07-05
  • Available Online: 2022-07-13
  • Publish Date: 2023-01-05
  • Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu_C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A, a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiNet-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.
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