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Hierarchical Retrieval of Ancient Chinese Character Images Based on Region Saliency and Skeleton Matching

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Web and Big Data (APWeb-WAIM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14331))

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Abstract

Ancient Chinese characters exhibit characteristics of complex structure and diverse styles. To address the limitations of traditional Chinese character image retrieval techniques when applied to ancient Chinese characters, this paper proposes a hierarchical retrieval method for ancient Chinese characters images based on region saliency and skeleton matching (RSSM). The proposed method utilizes saliency joint weighting algorithm to effectively integrate the channel and spatial dimension information of deep convolutional features, enhancing the representation of key features. It focuses on capturing the detailed features of Chinese character contours and spatial structure, enabling coarse-grained retrieval of ancient Chinese characters. Furthermore, to further enhance retrieval accuracy, an improved shape descriptor, skeleton context, is introduced for fine-grained matching. The retrieval results are organized in ascending order of matching cost. The study constructs an ancient Chinese character image dataset named GJHZ. The Precision and mAP of RSSM achieved \(90.71\%\) and \(90.59\%\), respectively. Experimental results demonstrate the superior performance of our method for ancient Chinese character image retrieval.

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Acknowledgements

We would like to thank anonymous reviewers for their helpful comments and suggestions. This work was supported by the Natural Science Foundation of Hebei Province of China (grant number F2019201329).

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Authors and Affiliations

  1. School of Cyber Security and Computer, Hebei University, Baoding, 071002, China

    Ruijuan Cai & Xuedong Tian

  2. Hebei Machine Vision Engineering Research Center, Hebei University, Baoding, 071002, China

    Ruijuan Cai & Xuedong Tian

  3. Institute of Intelligent Image and Document Information Processing, Hebei University, Baoding, 071002, China

    Ruijuan Cai & Xuedong Tian

Authors
  1. Ruijuan Cai
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  2. Xuedong Tian
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Corresponding author

Correspondence to Xuedong Tian .

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Editors and Affiliations

  1. Peng Cheng Laboratory, Shenzhen, China

    **angyu Song

  2. China University of Geosciences, Wuhan, China

    Ruyi Feng

  3. China University of Geosciences, Wuhan, China

    Yunliang Chen

  4. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  5. University of Exeter, Exeter, UK

    Geyong Min

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Cai, R., Tian, X. (2024). Hierarchical Retrieval of Ancient Chinese Character Images Based on Region Saliency and Skeleton Matching. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14331. Springer, Singapore. https://doi.org/10.1007/978-981-97-2303-4_18

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  • DOI: https://doi.org/10.1007/978-981-97-2303-4_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2302-7

  • Online ISBN: 978-981-97-2303-4

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