The most precious resource in such a pipeline is the diplomatist's time spent on annotating data. We drastically economize annotation effort by reformulating layout analysis as an object detection problem instead of the typical image segmentation approach. Indicatively, this allowed us to annotate the layout of 1175 charter images in a fraction of the time that would be needed normally. Figure 1 shows an example of this kind of annotation. Preliminary experiments demonstrate that this approach works well, e.g., it can detect seals with an accuracy above 95% when using a YOLOv5 (Jocher 2022) based model. With a 50% Intersection over Union (IoU) threshold this result is, of course, mainly usable for classification tasks, while segmentation will have to make use of approaches (Leipert et al. 2021). For tasks like HTR, writer identification, and layout analysis, we consider binarization as a useful step. We made experiments indicating that purely synthetic data could be used for the binarization step (Nicolaou 2022), yet comprehensive performance analysis on charters specifically would need manual annotation of the ground-truth pixel-by-pixel. Although the target CV pipeline will be under construction for a while, a few stand-alone methods required for such a pipeline have already been successfully developed and tested.