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Volume: 17 | Article ID: art00015
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Machine Learning and IIIF in the Reality Check of Daily Digitization Projects using the Example of the Goobi Community
  DOI :  10.2352/issn.2168-3204.2020.1.0.72  Published OnlineApril 2020
Abstract

Digital imaging, as an archival practice, is not a "solved problem" for the cultural heritage community. As Google, publishers, and other content providers digitize and deliver resources at scale, there is an increasingly pressing demand from users to digitize the rich resources in library special collections, archival institutions, and the vast array of invaluable content in private collections. This paper introduces a research and learning initiative (Dig4E-Digitization for Everybody) designed to bridge the knowledge gap that presently exists between well-established or emergent international standards derived from imaging science, on the one hand, and local practices for digital reformatting of archival resources. The paper describes the rationale for the education and training initiative and summarizes the intellectual structure and the technical platform of an innovative sequence of self-paced online resources that can be adapted for a variety of audiences.

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Steffen Hankiewicz, Oliver Paetzel, "Machine Learning and IIIF in the Reality Check of Daily Digitization Projects using the Example of the Goobi Communityin Proc. IS&T Archiving 2020,  2020,  pp 72 - 78,  https://doi.org/10.2352/issn.2168-3204.2020.1.0.72

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Copyright © Society for Imaging Science and Technology 2020
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