利用形态学进行历史文字计量分析

摘要

手写文本识别的进步使得大规模历史文献的转录成为可能，但在古文字学（即历史书写研究）领域，对可解释的视觉测量手段仍限于有限访问。本文的核心观点在于，形态学文字分析——特别是从行级转录中学习字符原型的能力——能够定义可扩展、有意义且稳定的古文字学测量指标。具体而言，我们利用基于Transformer的检测架构，结合基于原型的行重建模块，学习原型字符及其出现、变形和定位信息。我们的贡献体现在两个方面。首先，我们提出了一种深度架构与学习方法，仅需行级转录监督即可实现高效的字符建模，显著优于可学习打字机基线，并实现了精确的字符边界框预测，从而释放了其在古文字学测量中的潜力。其次，我们引入并展示了由该架构支持的自动测量方法在字符、二元组及图形单元间距方面的古文字学相关性。为进行演示，我们扩展了巴黎手稿BnF fr. 2813（14世纪末由查理五世委托制作、由四位抄写员完成的抄本）的注释，覆盖至160页。通过在这些页面中可视化测量结果，我们不仅能够区分图形轮廓，还能发现并分析细微变化。这一案例研究凸显了我们方法的可扩展性及其对训练数据的节俭性——仅需单列文本即可对160页中每一页进行计算测量。数据和代码已公开提供，详见：https://malamatenia.github.io/morphology4metrology-analysis

English

Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretable visual measurements for paleography, the study of historical scripts. In this paper, our main insight is that morphological script analysis, in particular the capacity to learn character prototypes from line-level transcriptions, enables the definition of scalable, meaningful, and stable paleographic measurements. More precisely, we leverage a transformer-based detection architecture together with a prototype-based line reconstruction module to learn prototypical characters and their occurrence, deformation, and positioning. Our contributions are twofold. First, we introduce a deep architecture and learning methodology that enables efficient character modeling with only line-level transcription supervision, significantly improving over the Learnable Typewriter baseline and enabling accurate character bounding box prediction, unlocking its potential for paleographic measurements. Second, we introduce and demonstrate the paleographical relevance of automatic measurements enabled by our architecture for characters, bi-grams, and spaces between graphical units. For this demonstration, we extend the annotations of the codex Paris, BnF, fr. 2813, commissioned in the late fourteenth century by Charles V and copied by four hands, to 160 pages. We visualize our measurements over these pages, showing how they enable us not only to differentiate graphical profiles, but also to discover and analyze subtle variations. This case study outlines the scalability of our approach and its frugality in terms of required training data, since a single column of text is sufficient to compute our measurements on each of the 160 pages. Data and code are publicly available at: https://malamatenia.github.io/morphology4metrology-analysis.