Machine Learning Meets Provenance Research: Recognising and Transcribing Handwritten Annotations in Auction Catalogues

Provenance research studies the origin and ownership history of objects including the conditions under which a change in ownership took place. In order to reconstruct the provenance of objects, provenance researchers utilise sources such as archival records, literature and online databases as well as examining the object itself. One essential online database for researchers is German Sales, which contains digitised auction catalogues from mostly German-speaking countries in the period from 1901 to 1945. Several catalogues contain handwritten annotations recording buyers, consignors and prices. However, in contrast to the printed text, the handwritten annotations are currently not searchable. To aid provenance researchers, it is imperative that the annotations be made machine-readable and searchable, which requires transcription, standardisation and enrichment of the handwritten notes. This article tests whether existing platforms, namely Transkribus, eScriptorium and ChatGPT, can be facilitated for the recognition and transcription of handwritten notes. While the experiments show the great potential of these methods, they also emphasise the need to train new models on these auction catalogues’ data.