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An intelligent user interface for efficient semi-automatic transcription of historical handwritten documents
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0003-1054-2754
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0003-4480-3158
2018 (English)In: Proc. 23rd International Conference on Intelligent User Interfaces Companion, New York: ACM Press, 2018, article id 48Conference paper, Published paper (Refereed)
Abstract [en]

Transcription of large-scale historical handwritten document images is a tedious task. Machine learning techniques, such as deep learning, are popularly used for quick transcription, but often require a substantial amount of pre-transcribed word examples for training. Instead of line-by-line word transcription, this paper proposes a simple training-free gamification strategy where all occurrences of each arbitrarily selected word is transcribed once, using an intelligent user interface implemented in this work. The proposed approach offers a fast and user-friendly semi-automatic transcription that allows multiple users to work on the same document collection simultaneously.

Place, publisher, year, edition, pages
New York: ACM Press, 2018. article id 48
National Category
Computer and Information Sciences
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-345637DOI: 10.1145/3180308.3180357ISI: 000458680100048ISBN: 978-1-4503-5571-1 (print)OAI: oai:DiVA.org:uu-345637DiVA, id: diva2:1189529
Conference
IUI 2018, March 7–11, Tokyo, Japan
Projects
eSSENCE
Funder
Riksbankens Jubileumsfond, NHS14-2068:1eSSENCE - An eScience CollaborationAvailable from: 2018-03-05 Created: 2018-03-12 Last updated: 2019-03-11Bibliographically approved

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Hast, Anders

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf