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 a...
| Published in: | International Journal of Humanities & Arts Computing: A Journal of Digital Humanities Vol. 19; no. 1; pp. 17 - 33 |
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| Format: | Article |
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Edinburgh University Press
Mar2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=183293252&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 183293252 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 17538548 2QD7 jtl: International Journal of Humanities & Arts Computing: A Journal of Digital Humanities issn: 17538548 maglogo: N pubinfo: dt: Mar2025 vid: 19 iid: 1 pid: 2327 pub: Edinburgh University Press artinfo: ui: 183293252 10.3366/ijhac.2025.0342 ppf: 17 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 507KB tig: atl: Machine Learning Meets Provenance Research: Recognising and Transcribing Handwritten Annotations in Auction Catalogues. aug: au: Lang, Sabine affil: Friedrich-Alexander-Universität Erlangen-Nürnberg su: Online databases Research personnel Machine learning ChatGPT Archival resources sug: subj: Online databases Research personnel Machine learning ChatGPT Archival resources keyword: handwriting recognition handwriting transcription machine learning provenance research ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of International Journal of Humanities & Arts Computing: A Journal of Digital Humanities is the property of Edinburgh University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: International Journal of Humanities & Arts Computing: A Journal of Digital Humanities holder: Edinburgh University Press dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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