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<p>Yi script character detection in ancient manuscripts is challenging due to limited annotated data and complex text structures, including arbitrary layouts, high character density, and non-standard forms. To address these challenges, we propose CloYiDet, a Yi script character detection method that incorporates a dual-branch transformer (Cloformer) for global-local feature fusion, enhancing feature representation by modeling both global semantic and local details. A text kernel stretching (TKS) is introduced to improve the model’s adaptability to varying aspect ratios and dense layouts. To address the data scarcity, a dedicated handwritten Yi script character dataset was constructed from ancient manuscripts. Experiments show that the proposed method achieves a precision of 99.63% and 98.32% on the Yi Handwritten and the Yi Print Dataset, respectively, demonstrating superior performance in detection tasks.</p>

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Yi script character detection in ancient manuscripts using a dual branch transformer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Xueyan Ding,
  • Zimeng Li,
  • Hua Yu,
  • Jianxin Zhang

摘要

Yi script character detection in ancient manuscripts is challenging due to limited annotated data and complex text structures, including arbitrary layouts, high character density, and non-standard forms. To address these challenges, we propose CloYiDet, a Yi script character detection method that incorporates a dual-branch transformer (Cloformer) for global-local feature fusion, enhancing feature representation by modeling both global semantic and local details. A text kernel stretching (TKS) is introduced to improve the model’s adaptability to varying aspect ratios and dense layouts. To address the data scarcity, a dedicated handwritten Yi script character dataset was constructed from ancient manuscripts. Experiments show that the proposed method achieves a precision of 99.63% and 98.32% on the Yi Handwritten and the Yi Print Dataset, respectively, demonstrating superior performance in detection tasks.