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<p>Oracle bone inscriptions provide critical insights into ancient Chinese history. However, the retrieval and analysis of inscription rubbings remain challenging due to fragmentation, weathering, and non-standardized character forms. These challenges fundamentally limit the applicability of conventional image retrieval methods, an issue exacerbated by the lack of large-scale annotated datasets. To tackle these challenges, we introduce the first dataset and a Multi-step Strategy for Homologous Rubbing Retrieval (MSHRR). MSHRR employs a three-stage pipeline integrating character extraction, cross-rubbing matching, and similarity scoring, bypassing Optical Character Recognition (OCR) dependencies. This novel framework outperforms state-of-the-art methods in handling glyph structures through its morphology-aware paradigm. More importantly, MSHRR has found 276 new homologous sets, accounting for over 10% of documented cases in twenty years. Our benchmark also offers a reproducible evaluation framework for computational archeology and reveals new historical connections.</p>

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An open benchmark for oracle bone rubbing image retrieval

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月24日

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  • Bang Li,
  • Zengmao Ding,
  • Yan Zhang,
  • Han Zhang,
  • Jing Yang,
  • Jiaqi Fang,
  • Taisong Jin,
  • Yongge Liu,
  • Zhenhao Song,
  • Rongrong Ji

摘要

Oracle bone inscriptions provide critical insights into ancient Chinese history. However, the retrieval and analysis of inscription rubbings remain challenging due to fragmentation, weathering, and non-standardized character forms. These challenges fundamentally limit the applicability of conventional image retrieval methods, an issue exacerbated by the lack of large-scale annotated datasets. To tackle these challenges, we introduce the first dataset and a Multi-step Strategy for Homologous Rubbing Retrieval (MSHRR). MSHRR employs a three-stage pipeline integrating character extraction, cross-rubbing matching, and similarity scoring, bypassing Optical Character Recognition (OCR) dependencies. This novel framework outperforms state-of-the-art methods in handling glyph structures through its morphology-aware paradigm. More importantly, MSHRR has found 276 new homologous sets, accounting for over 10% of documented cases in twenty years. Our benchmark also offers a reproducible evaluation framework for computational archeology and reveals new historical connections.