An Interpretable Deep Learning Approach for Morphological Script Type Analysis
This addresses a domain-specific problem in palaeography by providing a more objective method for script type analysis, though it is incremental as it adapts existing deep learning techniques to this niche application.
The paper tackles the problem of subjective and methodologically challenging classification of medieval handwriting script types by proposing an interpretable deep learning approach, resulting in systematic and objective analysis with tools for qualitative and quantitative comparison of character prototypes.
Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation method to learn comparable character prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the Textualis Formata script type and its two subtypes formalized by A. Derolez: Northern and Southern Textualis