Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation

TL;DR

This paper presents a novel multimodal framework that combines vision and language models to interpret Oracle Bone Script by leveraging its component structure, significantly improving decipherment accuracy.

Contribution

It introduces an agent-driven VLM framework and OB-Radix dataset to enhance structural understanding and semantic interpretation of ancient Chinese characters.

Findings

Outperforms baseline methods on multiple benchmarks.

Provides detailed and accurate decipherments.

Introduces a new dataset with structural and semantic annotations.

Abstract

Deciphering ancient Chinese Oracle Bone Script (OBS) is a challenging task that offers insights into the beliefs, systems, and culture of the ancient era. Existing approaches treat decipherment as a closed-set image recognition problem, which fails to bridge the ``interpretation gap'': while individual characters are often unique and rare, they are composed of a limited set of recurring, pictographic components that carry transferable semantic meanings. To leverage this structural logic, we propose an agent-driven Vision-Language Model (VLM) framework that integrates a VLM for precise visual grounding with an LLM-based agent to automate a reasoning chain of component identification, graph-based knowledge retrieval, and relationship inference for linguistically accurate interpretation. To support this, we also introduce OB-Radix, an expert-annotated dataset providing structural and semantic data absent from prior corpora, comprising 1,022 character images (934 unique characters) and 1,853 fine-grained component images across 478 distinct components with verified explanations. By evaluating our system across three benchmarks of different tasks, we demonstrate that our framework yields more detailed and precise decipherments compared to baseline methods.