# Deciphering Oracle Bone Language with Diffusion Models

arXiv: https://arxiv.org/abs/2406.00684 · HTML: https://mlsanity.com/paper/2406.00684/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [DRIT++: Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1905.01270/) (2019)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning](https://mlsanity.com/paper/2312.12142/) (2023)
- [An open dataset for the evolution of oracle bone characters: EVOBC](https://mlsanity.com/paper/2401.12467/) (2024)
- [Unsupervised Structure-Texture Separation Network for Oracle Character Recognition](https://mlsanity.com/paper/2205.06549/) (2022)

## Built on by (most cited first, 18 of 18)

- [Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research](https://mlsanity.com/paper/2412.04497/) (2024)
- [OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?](https://mlsanity.com/paper/2412.01175/) (2024)
- [OracleFusion: Assisting the Decipherment of Oracle Bone Script with Structurally Constrained Semantic Typography](https://mlsanity.com/paper/2506.21101/) (2025)
- [Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark](https://mlsanity.com/paper/2504.09555/) (2025)
- [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)
- [Interpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs](https://mlsanity.com/paper/2508.10113/) (2025)
- [Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning](https://mlsanity.com/paper/2408.06811/) (2024)
- [A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions](https://mlsanity.com/paper/2409.06381/) (2024)
- [InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling](https://mlsanity.com/paper/2508.15791/) (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)
- [Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors](https://mlsanity.com/paper/2601.05508/) (2026)
- [Towards Computational Chinese Paleography](https://mlsanity.com/paper/2601.06753/) (2026)
- [How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications](https://mlsanity.com/paper/2601.17020/) (2026)
- [OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training](https://mlsanity.com/paper/2606.25906/) (2026)
- [AlphaOracle: Oracle bone script decipherment via human-workflow-inspired deep learning](https://mlsanity.com/paper/2607.17849/) (2026)
- [FROD: Feature Matching Residual Denoising Oracle Bone Decipher](https://mlsanity.com/paper/2609.17227/) (2026)

## Abstract (arXiv)

Originating from China's Shang Dynasty approximately 3,000 years ago, the Oracle Bone Script (OBS) is a cornerstone in the annals of linguistic history, predating many established writing systems. Despite the discovery of thousands of inscriptions, a vast expanse of OBS remains undeciphered, casting a veil of mystery over this ancient language. The emergence of modern AI technologies presents a novel frontier for OBS decipherment, challenging traditional NLP methods that rely heavily on large textual corpora, a luxury not afforded by historical languages. This paper introduces a novel approach by adopting image generation techniques, specifically through the development of Oracle Bone Script Decipher (OBSD). Utilizing a conditional diffusion-based strategy, OBSD generates vital clues for decipherment, charting a new course for AI-assisted analysis of ancient languages. To validate its efficacy, extensive experiments were conducted on an oracle bone script dataset, with quantitative results demonstrating the effectiveness of OBSD. Code and decipherment results will be made available at https://github.com/guanhaisu/OBSD.