Towards Computational Chinese Paleography

TL;DR

This paper reviews the emerging field of computational Chinese paleography, highlighting technological advances, current challenges, and proposing future directions for AI-driven scholarly tools in analyzing ancient Chinese scripts.

Contribution

It provides a comprehensive overview of digital resources, methodological pipelines, and technological shifts, and advocates for multimodal, human-centric AI systems in Chinese paleography.

Findings

Analysis of key datasets for ancient Chinese scripts

Transition from classical vision to deep learning methods

Identification of core challenges like data scarcity

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.