  In this paper, we present a pipeline for image extraction from historical
documents using foundation models, and evaluate text-image prompts and their
effectiveness on humanities datasets of varying levels of complexity. The
motivation for this approach stems from the high interest of historians in
visual elements printed alongside historical texts on the one hand, and from
the relative lack of well-annotated datasets within the humanities when
compared to other domains. We propose a sequential approach that relies on
GroundDINO and Meta's Segment-Anything-Model (SAM) to retrieve a significant
portion of visual data from historical documents that can then be used for
downstream development tasks and dataset creation, as well as evaluate the
effect of different linguistic prompts on the resulting detections.
