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An open dataset for oracle bone script recognition and decipherment

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arxiv 2401.15365 v4 pith:5KK5QNBR submitted 2024-01-27 cs.CV

Pengjie Wang , Kaile Zhang , Xinyu Wang , Shengwei Han , Yongge Liu , Jinpeng Wan , Haisu Guan , Zhebin Kuang
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Lianwen Jin Xiang Bai Yuliang Liu
This is my paper · ORCID
classification cs.CV
keywords datasetbonecharactersdecipheringimagesoracleancientassist
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Oracle bone script, one of the earliest known forms of ancient Chinese writing, presents invaluable research materials for scholars studying the humanities and geography of the Shang Dynasty, dating back 3,000 years. The immense historical and cultural significance of these writings cannot be overstated. However, the passage of time has obscured much of their meaning, presenting a significant challenge in deciphering these ancient texts. With the advent of Artificial Intelligence (AI), employing AI to assist in deciphering Oracle Bone Characters (OBCs) has become a feasible option. Yet, progress in this area has been hindered by a lack of high-quality datasets. To address this issue, this paper details the creation of the HUST-OBC dataset. This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources. The hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs. All the codes and datasets are available at https://github.com/Yuliang-Liu/Open-Oracle.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PictOBI-20k: Unveiling Large Multimodal Models in Visual Decipherment for Pictographic Oracle Bone Characters

    cs.CV 2025-09 conditional novelty 6.0 of 10

    PictOBI-20k, a new 15k-question benchmark, shows top large multimodal models reach only 53.7% accuracy at matching oracle bone pictographs to object photos, with vision encoders often outperforming the full models.

  2. OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?

    cs.CV 2024-12 conditional novelty 6.0 of 10

    OBI-Bench evaluates 23 large multimodal models on five oracle bone inscription tasks and finds they lag on fine-grained perception but approach untrained-human level in deciphering.

  3. OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions

    cs.CV 2025-04 conditional novelty 5.0 of 10

    OBIFormer reports state-of-the-art PSNR and SSIM on oracle bone inscription denoising benchmarks while using fewer parameters than prior transformer-based methods.

  4. Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts

    cs.CL 2025-01 conditional novelty 5.0 of 10

    Current vision-language and text-only models perform poorly on deciphering non-Unicode rare scripts, and Unicode encoding helps only for common languages, not low-resource ones.

  5. OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion

    cs.CV 2024-11 reject novelty 5.0 of 10

    OracleSage, a LLaVA-based framework with hierarchical visual features and graph-based semantic reasoning, reaches 20.2% top-1 accuracy on the new OracleSem dataset, far below standard classifiers at around 90%.

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