用AI重构古文字研究，打造智能数字学术生态。
Towards Computational Chinese Paleography
- 从图像修复到智能解码，构建全流程数字研究链。
- 聚焦甲骨、青铜器、简牍三类核心资料，推动跨模态分析。
- 适合古文字学与AI交叉研究者，强调人机协作与少样本学习。
古文字学正迎来由人工智能驱动的计算变革。本文梳理该新兴领域的发展脉络，指出其正从孤立的视觉任务自动化转向集成化的数字学术生态系统。我们首先盘点了甲骨文、青铜器铭文和简牍文书等关键数字资源，系统分析了从基础视觉处理（如图像修复、字符识别），到上下文分析（如文物拼合、断代），再到高级推理（如自动释读、人机协同）的方法论流程。文章探讨了技术范式从传统计算机视觉向现代深度学习（包括Transformer与大模型）的演进。最后，我们总结了当前核心挑战：数据稀缺性，以及现有AI能力与人文研究整体性之间的脱节，并呼吁未来研究应聚焦于多模态、少样本及以人为本的系统建设，以增强学术研究能力。
原文摘要 · Abstract (English)
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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