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ROBC: A Radical-Level Oracle Bone Character Dataset

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15037))

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Abstract

Oracle bone characters (OBCs) serve as vital resources for the in-depth study of Chinese history and the development of writing systems. The recognition of OBCs holds immense significance in the realm of oracle research. Despite the growing adoption of deep learning techniques for OBC recognition, their widespread implementation has been hindered by challenges such as category imbalance and inaccurate labeling in existing datasets. We construct a radical-level oracle bone character dataset (ROBC) in response to these challenges. To mitigate the issue of inaccurate labeling, we rigorously clean the existing dataset based solely on glyph criteria. Moreover, we pioneer the annotation of radical-level information in the oracle bone character dataset. Through statistical analysis, we demonstrate the efficacy of radical-level annotations in alleviating the class imbalance issue prevalent in existing OBC datasets. In addition, we conduct closed and open set recognition tasks on the ROBC using multiple baseline models and achieve considerable results, demonstrating the versatility and robustness of the ROBC dataset and laying the foundation for future research in the OBC recognition. The ROBC dataset is temporarily available at https://github.com/ycfang-lab/ROBC.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China under Grant No.: 61976132 and 61991411. This work is supported by Shanghai Technical Service Center of Science and Engineering Computing, Shanghai University.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Zhengchen Li, Xintong Li, Kaiwen Qian & Yuchun Fang

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  1. Zhengchen Li
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  2. Xintong Li
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  3. Kaiwen Qian
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  4. Yuchun Fang
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Corresponding author

Correspondence to Yuchun Fang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Li, Z., Li, X., Qian, K., Fang, Y. (2025). ROBC: A Radical-Level Oracle Bone Character Dataset. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15037. Springer, Singapore. https://doi.org/10.1007/978-981-97-8511-7_8

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  • DOI: https://doi.org/10.1007/978-981-97-8511-7_8

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