Leveraging Morphology for Historical Script Metrological Analysis

2026-06-08

AI Summary

This study addresses the challenge that existing handwritten text recognition methods lack interpretable visual metrics suitable for paleographic analysis. The authors propose a novel architecture requiring only line-level transcription supervision, integrating a Transformer-based detection model, prototype-driven character representation learning, and a line-level reconstruction module to enable weakly supervised character localization and deformation modeling. This approach is the first to support automated paleographic measurements of individual characters, bigrams, and inter-character spacing. Evaluated on 160 pages from the 14th-century manuscript BnF fr. 2813, the method effectively distinguishes scribal styles and reveals subtle writing variations using only single-column text, significantly outperforming baselines such as Learnable Typewriter. Code and data are publicly released.

Abstract

Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretable visual measurements for paleography, the study of historical scripts.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.

Problem

• paleography
• historical script
• morphological analysis
• visual measurements
• handwritten text recognition

Innovation

• morphological analysis
• prototype learning
• historical script metrology
• transformer-based detection
• paleographic measurement