Understanding a collection item’s history, whether it is a painting in an art museum, a specimen in a natural history collection, or documents in an archive, is an essential part of responsible stewardship of that object. Unlike with digital files in asset management systems, unless there is active work to preserve a paper trail of ownership and location, this information is quickly lost. Heritage organizations maintain information about their accessioned collections, however this is primarily done with handwritten notes, letters and other correspondence, clippings from newspapers or auction catalogs, or printed out documents in a folder. These auxiliary files are both extensive and hard to use, as they require physical presence and to know where to look. They also show only a small part of the object’s history, with often significant events such as exhibitions or conservation processes being kept in different systems, or by different organizations. The research topic to be explored is the extent to which AI can accelerate provenance research processes, including especially the use of Large Language Model based transcription of printed and handwritten documents, and subsequent extraction of knowledge from the transcribed text as to the people, places, organizations and events in which the object participated.
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