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In this paper, we present a pipeline for image extraction from historicaldocuments using foundation models, and evaluate text-image prompts and theireffectiveness on humanities datasets of varying levels of complexity. Themotivation for this approach stems from the high interest of historians invisual elements printed alongside historical texts on the one hand, and fromthe relative lack of well-annotated datasets within the humanities whencompared to other domains. We propose a sequential approach that relies onGroundDINO and Meta's Segment-Anything-Model (SAM) to retrieve a significantportion of visual data from historical documents that can then be used fordownstream development tasks and dataset creation, as well as evaluate theeffect of different linguistic prompts on the resulting detections.

Prompt me a Dataset: An investigation of text-image prompting for historical image dataset creation using foundation models