Speakers
Description
This paper presents a practical approach to applying the Iconclass system to medieval manuscript imagery by combining image analysis with existing textual metadata. Building on our earlier large-language-model (LLM) pipeline for assigning Iconclass codes to early modern woodcuts, we extend the method to the Wenzelsbibel, a richly illuminated fourteenth-century German Bible.
The project tests how combining image data with existing metadata, such as image captions, editorial notes, and TEI transcriptions, can improve automated iconographic classification. Each miniature is processed through a Retrieval-Augmented Generation (RAG) workflow in which an LLM generates a description informed by both the image and its associated texts. These descriptions are then used to retrieve candidate Iconclass entries from a vector database. The model selects the best match, taking into account both specific and broader correspondences within Iconclass’s hierarchy. Our evaluation framework explicitly values partial matches, recognising that identifying a correct parent category (e.g. “Story of David and Goliath”) is still highly meaningful for cataloguing and search.
We also test text-only variants, using captions or descriptions without the image, to explore whether language alone can yield reliable iconographic assignments—a scenario relevant for digitized collections that already possess textual metadata but lack visual embeddings.
The broader aim is pragmatic as well as scholarly: to demonstrate a scalable, low-resource method that heritage institutions can use to enrich or standardize metadata without extensive manual annotation or machine-learning training. For collections with descriptive records but no controlled vocabulary, the pipeline offers a practical route to apply Iconclass consistently and transparently.
By integrating vision, language, and metadata, the project shows how AI can support rather than replace art-historical expertise, providing both a proof of concept for the Wenzelsbibel and a transferable model for iconographic description across collections.