Efficient Annotation of Medieval Charters
This work addresses the time-consuming annotation process in diplomatics for paleographers, though it appears incremental as it adapts existing object detection and regression techniques to this domain.
The paper tackles the problem of efficiently annotating medieval charters for segmentation by reducing it to object detection, which competes with or outperforms pixel-level segmentation in some cases and includes a method to predict physical lengths from image patches using regression neural networks.
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.