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On-the-fly historical handwritten text annotation
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
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
2017 (English)In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, 2017, p. 10-14Conference paper, Published paper (Refereed)
Abstract [en]

The performance of information retrieval algorithms depends upon the availability of ground truth labels annotated by experts. This is an important prerequisite, and difficulties arise when the annotated ground truth labels are incorrect or incomplete due to high levels of degradation. To address this problem, this paper presents a simple method to perform on-the-fly annotation of degraded historical handwritten text in ancient manuscripts. The proposed method aims at quick generation of ground truth and correction of inaccurate annotations such that the bounding box perfectly encapsulates the word, and contains no added noise from the background or surroundings. This method will potentially be of help to historians and researchers in generating and correcting word labels in a document dynamically. The effectiveness of the annotation method is empirically evaluated on an archival manuscript collection from well-known publicly available datasets.

Place, publisher, year, edition, pages
IEEE, 2017. p. 10-14
Series
Proceedings of the International Conference on Document Analysis and Recognition, E-ISSN 2379-2140
National Category
Computer Sciences
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-334296DOI: 10.1109/ICDAR.2017.374ISI: 000428139100002ISBN: 978-1-5386-3586-5 (electronic)OAI: oai:DiVA.org:uu-334296DiVA, id: diva2:1159239
Conference
14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, November 09-15, 2017
Funder
Riksbankens Jubileumsfond, NHS14-2068:1eSSENCE - An eScience CollaborationAvailable from: 2018-01-29 Created: 2017-11-22 Last updated: 2019-02-28Bibliographically approved

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Vats, EktaHast, Anders

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CiteExportLink to record
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  • apa
  • ieee
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Language
  • de-DE
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf