dorsal/arxiv
View SchemaTowards Computational Chinese Paleography
| Authors | Yiran Rex Ma |
|---|---|
| Categories | |
| ArXiv ID | 2601.06753vv1 |
| URL | https://arxiv.org/abs/2601.06753 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
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"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\u0027s 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\u0027s 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.",
"arxiv_id": "2601.06753",
"authors": [
"Yiran Rex Ma"
],
"categories": [
"cs.CL"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Towards Computational Chinese Paleography",
"url": "https://arxiv.org/abs/2601.06753",
"version": "v1"
},
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