Speakers
Description
This paper addresses the application of deep learning tools to the charter-specific platform Monasterium.net, with particular focus on the “Illuminated Charters” collection. Building on the object detection pipeline developed within the project DiDip, we extend its application toward art-historical and diplomatist analysis, a task previously requiring prohibitive manual effort.
The DiDip pipeline applies object detection and layout classification to over one million charter images, assigning detected regions to ten defined classes. This allows distinguishing ‘Writable Area’ from photographic artifacts, or ‘Seal’ from later additions like archival stamps (‘NewOther’). Our contribution lies in making these detections usable for scholarly inquiry: we integrate model predictions into the upcoming Monasterium virtual research environment, providing a faceted browsing and discovery interface by exploiting associated metadata. This enables experts to evaluate results qualitatively—revealing, for instance, that ‘NewOther’ contains an almost complete set of rotae, the distinctive authentication signs of solemn papal privileges. Such systematic access opens new avenues for tracing decorative conventions. For the sake of demonstration, results concerning litterae and their makers are presented and critically discussed.
From an art-historical and diplomatic perspective, such interfaces prove productive for identifying visual patterns across a given corpus, yet meaningful results require iterative refinement through expert guidance. Model-generated classifications remain provisional; their scholarly utility depends on applications enabling users to reorganize, filter, and interpret results in domain-appropriate ways. Nonetheless, it is an example of transforming raw detections into a discovery tool for comparative iconographic research.
Keywords: object detection, illuminated charters, Monasterium.net, layout classification, diplomatic studies, papal chancery