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Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains

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OBIMD: A Multi-modal Dataset for Contextual Interpretation of Oracle Bone Inscriptions.

Bang Li¹, Jing Yang¹, Yujie Liang²

¹Key Laboratory of Oracle Bone Inscriptions Information Processing, Ministry of Education of China, Anyang Normal University, Anyang, Henan, China.

Scientific Data

|March 14, 2026

Summary

This summary is machine-generated.

The Oracle Bone Inscriptions Multi-modal Dataset (OBIMD) offers a comprehensive digital resource for ancient Chinese writing. This dataset enhances research by providing images, annotations, and transcriptions for oracle bone inscriptions.

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Area of Science:

Linguistics
Archaeology
Computer Science

Background:

Oracle bone inscriptions represent the earliest Chinese writing system, crucial for historical and linguistic studies.
Existing digital resources lack contextual and structural data, hindering comprehensive analysis.

Purpose of the Study:

To introduce the Oracle Bone Inscriptions Multi-modal Dataset (OBIMD), a large-scale, publicly available corpus.
To provide pixel-aligned images, character annotations, and sentence transcriptions for oracle bone inscriptions.

Main Methods:

The OBIMD includes 10,077 images from five Shang Dynasty phases.
Features 93,652 annotated characters, 21,667 missing characters, 21,941 sentence units, and 4,192 non-sentential elements.
Integrates visual, structural, and linguistic data for multi-modal learning.

Main Results:

The dataset offers pixel-aligned rubbing and facsimile images.
Provides character-level annotations and sentence-level transcriptions with reading sequences.
Enables diverse tasks like facsimile enhancement and syntactic reconstruction.

Conclusions:

OBIMD is a foundational resource for recognizing and interpreting oracle bone inscriptions.
Facilitates scalable and systematic analysis of ancient Chinese writing.
Supports advanced multi-modal learning approaches for historical texts.