  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.
