# An open dataset for oracle bone script recognition and decipherment

arXiv: https://arxiv.org/abs/2401.15365 · HTML: https://mlsanity.com/paper/2401.15365/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Handwriting Recognition in Low-resource Scripts using Adversarial Learning](https://mlsanity.com/paper/1811.01396/) (2018)

## Built on by (most cited first, 19 of 19)

- [OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion](https://mlsanity.com/paper/2411.17837/) (2024)
- [Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction](https://mlsanity.com/paper/2406.03019/) (2024)
- [VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery](https://mlsanity.com/paper/2509.17191/) (2025)
- [Oracle Bone Inscriptions Multi-modal Dataset](https://mlsanity.com/paper/2407.03900/) (2024)
- [Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts](https://mlsanity.com/paper/2501.17785/) (2025)
- [PictOBI-20k: Unveiling Large Multimodal Models in Visual Decipherment for Pictographic Oracle Bone Characters](https://mlsanity.com/paper/2509.05773/) (2025)
- [MCCD: A Multi-Attribute Chinese Calligraphy Character Dataset Annotated with Script Styles, Dynasties, and Calligraphers](https://mlsanity.com/paper/2507.06948/) (2025)
- [Interpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs](https://mlsanity.com/paper/2508.10113/) (2025)
- [CoLa: Chinese Character Decomposition with Compositional Latent Components](https://mlsanity.com/paper/2506.03798/) (2025)
- [UniCalli: A Unified Diffusion Framework for Column-Level Generation and Recognition of Chinese Calligraphy](https://mlsanity.com/paper/2510.13745/) (2025)
- [Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation](https://mlsanity.com/paper/2604.06711/) (2026)
- [Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning](https://mlsanity.com/paper/2604.11299/) (2026)
- [Towards Computational Chinese Paleography](https://mlsanity.com/paper/2601.06753/) (2026)
- [AMR-CCR: Anchored Modular Retrieval for Continual Chinese Character Recognition](https://mlsanity.com/paper/2603.07497/) (2026)
- [Decoding Ancient Oracle Bone Script via Generative Dictionary Retrieval](https://mlsanity.com/paper/2604.09668/) (2026)
- [Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention](https://mlsanity.com/paper/2607.00057/) (2026)
- [AlphaOracle: Oracle bone script decipherment via human-workflow-inspired deep learning](https://mlsanity.com/paper/2607.17849/) (2026)
- [JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis](https://mlsanity.com/paper/2608.11741/) (2026)
- [FROD: Feature Matching Residual Denoising Oracle Bone Decipher](https://mlsanity.com/paper/2609.17227/) (2026)

## Abstract (arXiv)

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.