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<p>Ancient Chinese characters preserved in ancient books, calligraphy paintings, stones, and bronzes rubbings exhibit diverse morphological structures distinct from modern scripts, requiring specialized paleographic expertise for accurate recognition. However, few experts can recognize multiple character types simultaneously. This study constructed the Multi-Type Ancient Chinese Character Recognition (MTACCR) dataset and proposed HUNet, a Hierarchical Universal Network that employs a parameter-sharing architecture to achieve efficient recognition of diverse ancient character types through multi-stage feature extraction and fusion. The experimental results show that compared to other efficient models evaluated, the HUNet series achieves higher recognition performance with similar parameter counts (or maintains lower parameter counts at equivalent recognition performance) while sustaining high computational throughput. In addition, after training on MTACCR, HUNet’s Top-1 accuracy on the MACR test set improved by 9.64%. Therefore, HUNet can be easily deployed on different devices and platforms for efficiently recognizing multiple types of ancient Chinese characters.</p>

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HUNet: hierarchical universal network for multi-type ancient Chinese character recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月19日

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  • Zhaojiang Wang,
  • Chu Zhang,
  • Qing Lang,
  • Lianwen Jin,
  • Hengnian Qi

摘要

Ancient Chinese characters preserved in ancient books, calligraphy paintings, stones, and bronzes rubbings exhibit diverse morphological structures distinct from modern scripts, requiring specialized paleographic expertise for accurate recognition. However, few experts can recognize multiple character types simultaneously. This study constructed the Multi-Type Ancient Chinese Character Recognition (MTACCR) dataset and proposed HUNet, a Hierarchical Universal Network that employs a parameter-sharing architecture to achieve efficient recognition of diverse ancient character types through multi-stage feature extraction and fusion. The experimental results show that compared to other efficient models evaluated, the HUNet series achieves higher recognition performance with similar parameter counts (or maintains lower parameter counts at equivalent recognition performance) while sustaining high computational throughput. In addition, after training on MTACCR, HUNet’s Top-1 accuracy on the MACR test set improved by 9.64%. Therefore, HUNet can be easily deployed on different devices and platforms for efficiently recognizing multiple types of ancient Chinese characters.