Speaker
Description
The analysis of medieval handwriting lies at the heart of palaeographical research. While Automatic Text Recognition (ATR) systems are primarily developed to produce transcriptions, they also generate rich data that can be leveraged to study the scripts themselves. This presentation will explore how ATR data can be exploited to develop new computational tools for palaeographical analysis. Starting from text-line images and their transcriptions, the proposed framework automatically detects and models character instances without requiring manual character-level annotation. It produces visual prototypes representing average letterforms, together with metrological descriptors encoding their dimensions and spatial positions within the text line. These representations provide quantitative descriptions of handwriting while remaining readily interpretable by palaeographers. The framework will be illustrated through several case studies in Latin palaeography, focusing on book scripts and covering different levels of analysis, including script-type comparison, scribal identification, intra-scribal handwriting variability, and the study of scribal digraphism.