# Towards Computational Chinese Paleography

arXiv: https://arxiv.org/abs/2601.06753 · HTML: https://mlsanity.com/paper/2601.06753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 100 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Builds on (most cited first, 25 of 25)

- [OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?](https://mlsanity.com/paper/2412.01175/) (2024)
- [An open dataset for oracle bone script recognition and decipherment](https://mlsanity.com/paper/2401.15365/) (2024)
- [Deciphering Oracle Bone Language with Diffusion Models](https://mlsanity.com/paper/2406.00684/) (2024)
- [AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation](https://mlsanity.com/paper/2303.07012/) (2023)
- [Unsupervised Structure-Texture Separation Network for Oracle Character Recognition](https://mlsanity.com/paper/2205.06549/) (2022)
- [RZCR: Zero-shot Character Recognition via Radical-based Reasoning](https://mlsanity.com/paper/2207.05842/) (2022)
- [RCRN: Real-world Character Image Restoration Network via Skeleton Extraction](https://mlsanity.com/paper/2207.07795/) (2022)
- [Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations](https://mlsanity.com/paper/2308.00655/) (2023)
- [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)
- [OracleFusion: Assisting the Decipherment of Oracle Bone Script with Structurally Constrained Semantic Typography](https://mlsanity.com/paper/2506.21101/) (2025)
- [CHisIEC: An Information Extraction Corpus for Ancient Chinese History](https://mlsanity.com/paper/2403.15088/) (2024)
- [Ancient Script Image Recognition and Processing: A Review](https://mlsanity.com/paper/2506.19208/) (2025)
- [A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond](https://mlsanity.com/paper/2411.11354/) (2024)
- [Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark](https://mlsanity.com/paper/2504.09555/) (2025)
- [PictOBI-20k: Unveiling Large Multimodal Models in Visual Decipherment for Pictographic Oracle Bone Characters](https://mlsanity.com/paper/2509.05773/) (2025)
- [Diff-Oracle: Deciphering Oracle Bone Scripts with Controllable Diffusion Model](https://mlsanity.com/paper/2312.13631/) (2023)
- [Clustering-based Feature Representation Learning for Oracle Bone Inscriptions Detection](https://mlsanity.com/paper/2508.18641/) (2025)
- [CoLa: Chinese Character Decomposition with Compositional Latent Components](https://mlsanity.com/paper/2506.03798/) (2025)
- [BIRD: Bronze Inscription Restoration and Dating](https://mlsanity.com/paper/2511.01589/) (2025)
- [Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN](https://mlsanity.com/paper/2306.02078/) (2023)
- [Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slip Scripts](https://mlsanity.com/paper/2409.01011/) (2024)
- [Explainable Coarse-to-Fine Ancient Manuscript Duplicates Discovery](https://mlsanity.com/paper/2505.03836/) (2025)
- [Rejoining fragmented ancient bamboo slips with physics-driven deep learning](https://mlsanity.com/paper/2505.08601/) (2025)
- [InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling](https://mlsanity.com/paper/2508.15791/) (2025)

## Abstract (arXiv)

Chinese paleography, the study of ancient Chinese writing, is undergoing a computational turn powered by artificial intelligence. This position paper charts the trajectory of this emerging field, arguing that it is evolving from automating isolated visual tasks to creating integrated digital ecosystems for scholarly research. We first map the landscape of digital resources, analyzing critical datasets for oracle bone, bronze, and bamboo slip scripts. The core of our analysis follows the field's methodological pipeline: from foundational visual processing (image restoration, character recognition), through contextual analysis (artifact rejoining, dating), to the advanced reasoning required for automated decipherment and human-AI collaboration. We examine the technological shift from classical computer vision to modern deep learning paradigms, including transformers and large multimodal models. Finally, we synthesize the field's core challenges -- notably data scarcity and a disconnect between current AI capabilities and the holistic nature of humanistic inquiry -- and advocate for a future research agenda focused on creating multimodal, few-shot, and human-centric systems to augment scholarly expertise.