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Towards Computational Chinese Paleography

Yiran Rex Ma

TL;DR

This work analyzes the emergence of computational Chinese paleography, tracing its shift from automated visual tasks to integrated, multimodal research ecosystems. It presents a problem-oriented pipeline spanning foundation image processing, contextual artifact analysis, and advanced reasoning with knowledge graphs and decipherment, while highlighting data scarcity and human–AI alignment as central challenges. The paper catalogs diverse datasets (oracle bone, bronze, bamboo) and surveys methods ranging from classical features to diffusion-based decipherment and LVLM-powered cross-modal reasoning, framing a future of human-centric, few-shot, multimodal tools that augment scholarly inquiry. The contribution lies in articulating a holistic research agenda, benchmarks, and infrastructure to unify data, methods, and human expertise for scalable, interpretable paleographic discovery with practical impact for archaeology, linguistics, and history.

Abstract

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.

Towards Computational Chinese Paleography

TL;DR

This work analyzes the emergence of computational Chinese paleography, tracing its shift from automated visual tasks to integrated, multimodal research ecosystems. It presents a problem-oriented pipeline spanning foundation image processing, contextual artifact analysis, and advanced reasoning with knowledge graphs and decipherment, while highlighting data scarcity and human–AI alignment as central challenges. The paper catalogs diverse datasets (oracle bone, bronze, bamboo) and surveys methods ranging from classical features to diffusion-based decipherment and LVLM-powered cross-modal reasoning, framing a future of human-centric, few-shot, multimodal tools that augment scholarly inquiry. The contribution lies in articulating a holistic research agenda, benchmarks, and infrastructure to unify data, methods, and human expertise for scalable, interpretable paleographic discovery with practical impact for archaeology, linguistics, and history.

Abstract

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.
Paper Structure (36 sections, 1 figure, 1 table)

This paper contains 36 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. From Classical Epigraphy to Computational Paleography
  3. The Scope, Materials, and Tasks of Chinese Paleography
  4. The Unique Challenge of Chinese Paleography
  5. The Dilemmas of a Golden Age
  6. The Computational Turn
  7. Resources and Datasets
  8. Character-Level Datasets
  9. Handwritten Oracle Bone Datasets
  10. Scanned Oracle Bone Datasets
  11. Datasets for Other Scripts
  12. Multimodal, Full-Document, and Complex-Scene Datasets
  13. Specialized and Task-Oriented Datasets
  14. Challenges in Data Curation
  15. Foundational Visual Analysis
  16. ...and 21 more sections

Figures (1)

  • Figure 1: The methodological progression charted in this paper, from foundational visual processing to advanced reasoning systems. The full structure, including introductory and concluding sections, is detailed in the main text.