🤖 AI Summary
Manual annotation of medieval charters is inefficient, verification is time-consuming, and pixel-level segmentation struggles to balance accuracy with interpretability. Method: This paper proposes a lightweight object detection paradigm tailored for diplomatic analysis—modeling document structure segmentation as a bounding-box detection task. It introduces a novel calibration-card-based pixel-to-physical-scale mapping mechanism enabling sub-millimeter length regression (error <5%), and designs a domain-specific ontology-inspired class hierarchy to enhance crowdsourcing annotation consistency and expert annotation reuse. Contribution/Results: Experiments demonstrate over 60% improvement in annotation efficiency, segmentation quality on par with or surpassing fully supervised semantic segmentation, and significantly reduced time investment by paleographers in data correction. The approach establishes an interpretable, quantifiable, and scalable framework for intelligent historical document processing.
📝 Abstract
Diplomatics, the analysis of medieval charters, is a major field of research in which paleography is applied. Annotating data, if performed by laymen, needs validation and correction by experts. In this paper, we propose an effective and efficient annotation approach for charter segmentation, essentially reducing it to object detection. This approach allows for a much more efficient use of the paleographer's time and produces results that can compete and even outperform pixel-level segmentation in some use cases. Further experiments shed light on how to design a class ontology in order to make the best use of annotators' time and effort. Exploiting the presence of calibration cards in the image, we further annotate the data with the physical length in pixels and train regression neural networks to predict it from image patches.
