OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates Discovery

MCML Authors

Matthias Aßenmacher

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

Oracle Bone Inscription (OBI) is the earliest systematic writing system in China, while the identification of Oracle Bone (OB) duplicates is a fundamental issue in OBI research. In this work, we design a progressive OB duplicate discovery framework that combines unsupervised low-level keypoints matching with high-level text-centric content-based matching to refine and rank the candidate OB duplicates with semantic awareness and interpretability. We compare our approach with state-of-the-art content-based image retrieval and image matching methods, showing that our approach yields comparable recall performance and the highest simplified mean reciprocal rank scores for both Top-5 and Top-15 retrieval results, and with significantly accelerated computation efficiency. We have discovered over 60 pairs of new OB duplicates in real-world deployment, which were missed by OBI researchers for decades.

misc

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Preprint

May. 2025

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Authors

C. Zhang • S. Wu • Y. Chen • M. Aßenmacher • C. Heumann • Y. Men • G. Fan • J. Gama

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Links

GitHub

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Research Area

 A1 | Statistical Foundations & Explainability