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Open-Oracle

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AI-assisted Deciphering Oracle Bone Script

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<h1 align="center">Open-Oracle</h1>

With the support of big data and AI, oracle bone script research has entered a new era. This repository curates open datasets, benchmarks, codebases, and papers for AI-assisted oracle bone inscription recognition, retrieval, rejoining, decipherment, and interpretation.

<h5 align="center">Please give us a star ⭐ for the latest updates.</h5>

Last maintained: 2026-07-28 
 <a href="PAPERS.md">Comprehensive paper index</a>

---

## Contents

- [Recent Oracle Bone Projects and Papers](#recent-oracle-bone-projects-and-papers)
- [Datasets and Benchmarks](#datasets-and-benchmarks)
- [Paper Index](#paper-index)
- [Online Resources](#online-resources)
- [Our Projects](#our-projects)
- [Contributing](#contributing)
- [Copyright](#copyright)

## Recent Oracle Bone Projects and Papers

This section lists representative recent oracle bone inscription work from all groups, including our own projects, ordered by year. See [`PAPERS.md`](PAPERS.md) for the more detailed task-oriented paper index.

## Datasets and Benchmarks

| Dataset or Benchmark | Task | Scale and Notes | Links |
| :--- | :--- | :--- | :--- |
| **HUST-OBC** | Recognition + decipherment | 140,053 images; deciphered and undeciphered character categories | [Paper](https://www.nature.com/articles/s41597-024-03807-x), [Code](https://github.com/Pengjie-W/HUST-OBC), [Data](https://figshare.com/s/8a9c0420312d94fc01e3) |
| **EVOBC** | Character evolution | Multi-period evolution data across OBC, BI, SS, SAC, WSC, CS | [Paper](https://arxiv.org/abs/2401.12467), [Code](https://github.com/RomanticGodVAN/character-Evolution-Dataset), [Data](https://figshare.com/s/ce2cf55b35a2f8ecc4c6) |
| **OBIMD** | Contextual interpretation | 10,077 OBI images; 93,652 annotated characters; sentence-level readings | [Paper](https://www.nature.com/articles/s41597-026-06967-0), [Code](https://github.com/libang1991/OBIMD), [HF](https://huggingface.co/datasets/KLOBIP/OBIMD) |
| **OBI-Bench** | LMM benchmark | Five OBI processing tasks; 5,523 images | [OpenReview](https://openreview.net/forum?id=hL5jone2Oh), [Code](https://github.com/zijianchen98/OBI-Bench) |
| **PictOBI-20k** | Visual decipherment | 20k OBC-object image pairs; 15k+ multi-choice questions | [IEEE](https://ieeexplore.ieee.org/document/11462601), [arXiv](https://arxiv.org/abs/2509.05773), [Code](https://github.com/OBI-Future/PictOBI-20k) |
| **Chronicles-OCR** | Cross-temporal OCR | 2,800 balanced images across the Seven Chinese Scripts, including oracle bone script | [arXiv](https://arxiv.org/abs/2605.11960), [Code](https://github.com/VirtualLUOUCAS/Chronicles-OCR), [HF](https://huggingface.co/datasets/VirtualLUO/Chronicles-OCR) |
| **S-OBI** | Sentence-level OBI understanding | 95 standardized sentence-level OBI instances and 695 QA pairs for semantic matching, slot extraction, and contextual reasoning | [arXiv](https://arxiv.org/abs/2606.31169) |
| **Structure–semantic Dual-Alignment Benchmark** | LLM evaluation | Structure and semantic alignment benchmarking for ancient OBI understanding | [Paper](https://www.nature.com/articles/s40494-026-02770-7) |
| **OBB Benchmark** | Evidence-grounded OBS understanding | Benchmark and OBS-PRTF framework for evidence-grounded oracle bone script understanding | [Paper](https://doi.org/10.1007/978-981-92-3510-0_20) |
| **Parametric OBS SVG Resource** | Parametric representation | Dataset, adaptive arc segmentation, and SVG resources for oracle bone script | [Preprint](https://doi.org/10.21203/rs.3.rs-10028068/v1) |
| **Long-tail OBI Detection and Recognition Diagnostics** | Detection + recognition evaluation | Diagnostic benchmark and metric analysis for long-tailed oracle bone character detection and recognition | [Preprint](https://doi.org/10.21203/rs.3.rs-10039997/v1) |
| **Oracle-MNIST** | Benchmark classification | 30,222 grayscale oracle-character images in 10 categories | [Paper](https://www.nature.com/articles/s41597-024-02933-w), [Code](https://github.com/wm-bupt/oracle-mnist) |
| **Oracle-P15K** | Long-tail recognition | Long-tail OBI benchmark with synthesis-based augmentation | [Paper](https://dl.acm.org/doi/10.1145/3746027.3755067), [Code](https://github.com/OBI-Future/Oracle-P15K) |
| **PD-OBS** | Interpretable decipherment | Radical and pictographic annotations for LVLM training | [Paper](https://arxiv.org/abs/2508.10113), [Code](https://github.com/PKXX1943/PD-OBS) |
| **GEVOBC and GBEDOBC** | Evolution graph dataset | Graph-based evolutionary oracle bone character dataset | [Paper](https://www.nature.com/articles/s40494-025-01951-0), [Code](https://github.com/BrisksHan/GBEDOBC) |
| **OBI Rubbing Retrieval Benchmark** | Rubbing retrieval | Homologous rubbing retrieval benchmark | [Paper](https://www.nature.com/articles/s40494-025-01859-9) |
| **OBFI and OBID-ACR** | Fragment rejoining and bone association | Fragment-image and bone-level association benchmarks | [OBFI](https://www.nature.com/articles/s40494-025-01651-9), [OBID-ACR](https://www.nature.com/articles/s40494-025-02282-w) |
| **OBC306, HWOBC, YinQiWenYuan** | Recognition and detection | Public resources from Yin Qi Wen Yuan | [Website](https://jgw.aynu.edu.cn/home/down/index.html) |

## Paper Index

This README highlights representative works. For a broader task-oriented bibliography, see:

- 📚 **[`PAPERS.md`](PAPERS.md)**: surveys, knowledge resources, datasets, decipherment, multimodal reasoning, recognition, detection, segmentation, retrieval, rejoining, restoration, generation, and broader ancient-script processing.
- 🔎 **Recommended companion survey repo**: [OBI-Survey](https://github.com/OBI-Future/OBI-Survey).

## Online Resources

### OBI Websites and Databases

### Museum Collections

## Our Projects

### 📘 [The Innovation] AlphaOracle: Oracle Bone Script Decipherment via Human-Workflow-Inspired Deep Learning

Yuliang Liu, Haisu Guan, Pengjie Wang, Xinyu Wang, Jinpeng Wan, Kaile Zhang, Handong Zheng, Xingchen Liu, Zhebin Kuang, Huanxin Yang, Bang Li, Yongge Liu, Lianwen Jin, Xiang Bai.

[Paper](https://www.sciencedirect.com/science/article/pii/S2666675826002092) | [arXiv](https://arxiv.org/abs/2607.17849) | [GitHub Code](https://github.com/Yuliang-Liu/AlphaOracle)

AlphaOracle is published in **The Innovation**. AlphaOracle integrates computer vision, computational linguistics, and philological validation into a human-workflow-inspired framework for oracle bone script analysis and decipherment.

```BibTeX
@article{liu2026alphaoracle,
  title   = {AlphaOracle: Oracle Bone Script Decipherment via Human-Workflow-Inspired Deep Learning},
  author  = {Liu, Yuliang and Guan, Haisu and Wang, Pengjie and Wang, Xinyu and Wan, Jinpeng and Zhang, Kaile and Zheng, Handong and Liu, Xingchen and Kuang, Zhebin and Yang, Huanxin and Li, Bang and Liu, Yongge and Jin, Lianwen and Bai, Xiang},
  journal = {The Innovation},
  year    = {2026},
  url     = {https://www.sciencedirect.com/science/article/pii/S2666675826002092}
}
```

***

### 📘 [ACL 2024 Best Paper] Deciphering Oracle Bone Language with Diffusion Models

Haisu Guan, Huanxin Yang, Xinyu Wang, Shengwei Han, Yongge Liu, Lianwen Jin, Xiang Bai, Yuliang Liu.

[Paper](https://aclanthology.org/2024.acl-long.831/) | [arXiv](https://arxiv.org/abs/2406.00684) | [GitHub Code](https://github.com/guanhaisu/OBSD)

This paper introduces **Oracle Bone Script Decipher (OBSD)**, a conditional diffusion-based strategy that generates modern-character clues for oracle bone script decipherment.

```BibTeX
@inproceedings{guan2024deciphering,
  title     = {Deciphering Oracle Bone Language with Diffusion Models},
  author    = {Guan, Haisu and Yang, Huanxin and Wang, Xinyu and Han, Shengwei and Liu, Yongge and Jin, Lianwen and Bai, Xiang and Liu, Yuliang},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages     = {15554--15567},
  year      = {2024},
  doi       = {10.18653/v1/2024.acl-long.831}
}
```

***

### 📘 [ICDAR 2024 Oral] Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction

Pengjie Wang, Kaile Zhang, Xinyu Wang, Shengwei Han, Yongge Liu, Lianwen Jin, Xiang Bai, Yuliang Liu.

[Paper](https://arxiv.org/abs/2406.03019) | [GitHub Code](https://github.com/Pengjie-W/Puzzle-Pieces-Picker)

Puzzle Pieces Picker (P3) deconstructs oracle bone inscriptions into strokes and radicals and reconstructs them into modern counterparts with a Transformer-based model.

```BibTeX
@inproceedings{wang2024puzzle,
  title     = {Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction},
  author    = {Wang, Pengjie and Zhang, Kaile and Wang, Xinyu and Han, Shengwei and Liu, Yongge and Jin, Lianwen and Bai, Xiang and Liu, Yuliang},
  booktitle = {Document Analysis and Recognition -- ICDAR 2024},
  year      = {2024},
  publisher = {Springer},
  doi       = {10.1007/978-3-031-70533-5_11}
}
```

***

### 📘 [SCIENTIA SINICA Informationis 2026] A Multi-task Multimodal Reasoning Framework for Oracle Bone Character Interpretation

Jinpeng Wan, Yuliang Liu, Xiang Bai.

[Paper](http://engine.scichina.com/doi/10.1360/SSI-2025-0551) | [GitHub Code](https://github.com/RomanticGodVAN/OraclePrime.git) | [Data](https://drive.google.com/drive/folders/1ApeqdOZH_l_Dl3WZn1F_GGPcLCxe630m?usp=drive_link)

This paper introduces **ORACLE-PRIME**, a multi-task multimodal reasoning framework for oracle bone character interpretation. It integrates glyph perception, structural periodization, and evolutionary reasoning to generate philologically grounded reasoning chains.

```BibTeX
@article{wan2026oracleprime,
  title   = {A multi-task multimodal reasoning framework for Oracle Bone character interpretation},
  author  = {Wan, Jinpeng and Liu, Yuliang and Bai, Xiang},
  journal = {SCIENTIA SINICA Informationis},
  year    = {2026},
  pages   = {-},
  url     = {http://www.sciengine.com/publisher/Science China Press/journal/SCIENTIA SINICA Informationis///10.1360/SSI-2025-0551},
  doi     = {10.1360/SSI-2025-0551}
}
```

***

### 📘 [arXiv 2024] An Open Dataset for the Evolution of Oracle Bone Characters: EVOBC

Haisu Guan, Jinpeng Wan, Yuliang Liu, Pengjie Wang, Kaile Zhang, Zhebin Kuang, Xinyu Wang, Xiang Bai, Lianwen Jin.

[Paper](https://arxiv.org/abs/2401.12467) | [GitHub Code](https://github.com/RomanticGodVAN/character-Evolution-Dataset) | [Data](https://figshare.com/s/ce2cf55b35a2f8ecc4c6)

EVOBC collects character images across six historical stages: Oracle Bone Characters, Bronze Inscriptions, Seal Script, Spring and Autumn period characters, Warring States period characters, and Clerical Script.

```BibTeX
@article{guan2024open,
  title   = {An Open Dataset for the Evolution of Oracle Bone Characters: EVOBC},
  author  = {Guan, Haisu and Wan, Jinpeng and Liu, Yuliang and Wang, Pengjie and Zhang, Kaile and Kuang, Zhebin and Wang, Xinyu and Bai, Xiang and Jin, Lianwen},
  journal = {arXiv preprint arXiv:2401.12467},
  year    = {2024}
}
```

***

### 📘 [Scientific Data 2024] An Open Dataset for Oracle Bone Character Recognition and Decipherment

Pengjie Wang, Kaile Zhang, Xinyu Wang, Shengwei Han, Yongge Liu, Jinpeng Wan, Haisu Guan, Zhebin Kuang, Lianwen Jin, Xiang Bai, Yuliang Liu.

[Paper](https://www.nature.com/articles/s41597-024-03807-x) | [arXiv](https://arxiv.org/abs/2401.15365) | [GitHub Code](https://github.com/Pengjie-W/HUST-OBC) | [Data](https://figshare.com/s/8a9c0420312d94fc01e3) | [hyper.ai](https://hyper.ai/datasets/33506)

HUST-OBC is a large-scale open dataset for oracle bone character recognition and decipherment, containing deciphered and undeciphered OBC images.

```BibTeX
@article{wang2024open,
  title   = {An Open Dataset for Oracle Bone Character Recognition and Decipherment},
  author  = {Wang, Pengjie and Zhang, Kaile and Wang, Xinyu and Han, Shengwei and Liu, Yongge and Wan, Jinpeng and Guan, Haisu and Kuang, Zhebin and Jin, Lianwen and Bai, Xiang and Liu, Yuliang},
  journal = {Scientific Data},
  volume  = {11},
  number  = {1},
  year    = {2024},
  doi     = {10.1038/s41597-024-03807-x}
}
```

***

## Contributing

We welcome pull requests and issues. For new papers and resources, please include:

1. **Title**, **authors**, **venue and year**, and **task category**.
2. Official paper link, DOI, arXiv, or OpenReview page when available.
3. Code, data, or demo links if public.
4. A one-line summary of the contribution.

## Copyright

We welcome suggestions to help improve Open-Oracle. For any query, please contact Prof. Yuliang Liu: ylliu@hust.edu.cn. If you find something interesting, feel free to share it by email or open an issue. Thanks!

Open-Oracle

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