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SFF-Siam: A New Oracle Bone Rejoining Method Based on Siamese Network.

Jiang Yuan, Chen Shanxiong, Gao Weize

    IEEE Computer Graphics and Applications
    |June 14, 2023
    PubMed
    Summary

    Researchers developed a new AI model for rejoining oracle bone fragments, significantly improving efficiency and accuracy in oracle bone inscription (OBI) research. This method accelerates the analysis of ancient texts.

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    Area of Science:

    • Digital Humanities
    • Artificial Intelligence
    • Archaeology

    Background:

    • Rejoining oracle bone fragments is crucial for oracle bone inscription (OBI) research.
    • Traditional methods are slow, labor-intensive, and unsuitable for large-scale applications.

    Purpose of the Study:

    • To develop an efficient and accurate automated method for rejoining oracle bone fragments.
    • To address the limitations of traditional OB rejoining techniques.

    Main Methods:

    • Proposed a simple OB rejoining model (SFF-Siam).
    • Utilized a similarity feature fusion (SFF) module to combine input features.
    • Employed a backbone feature extraction network for similarity evaluation.
    • Incorporated a forward feedback network to predict rejoining probability.

    Main Results:

    • The SFF-Siam model demonstrated significant effectiveness in OB rejoining.
    • Achieved average accuracies of 96.4% and 90.1% on benchmark datasets.
    • Indicates high potential for AI in OBI research.

    Conclusions:

    • The SFF-Siam model offers a viable solution for large-scale OB rejoining.
    • This AI-driven approach can accelerate OBI research and analysis.
    • Promotes the integration of AI technology with ancient inscription studies.