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A novel word segmentation method based on object detection and deep learning
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.
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: 0000-0002-4405-6888
2015 (English)In: Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I / [ed] Bebis, G; Boyle, R; Parvin, B; Koracin, D; Pavlidis, I; Feris, R; McGraw, T; Elendt, M; Kopper, R; Ragan, E; Ye, Z; Weber, G, Springer, 2015, 231-240 p.Conference paper, Published paper (Refereed)
Abstract [en]

The segmentation of individual words is a crucial step in several data mining methods for historical handwritten documents. Examples of applications include visual searching for query words (word spotting) and character-by-character text recognition. In this paper, we present a novel method for word segmentation that is adapted from recent advances in computer vision, deep learning and generic object detection. Our method has unique capabilities and it has found practical use in our current research project. It can easily be trained for different kinds of historical documents, uses full gray scale information, does not require binarization as pre-processing or prior segmentation of individual text lines. We evaluate its performance using established error metrics, previously used in competitions for word segmentation, and demonstrate its usefulness for a 15th century handwritten document.

Place, publisher, year, edition, pages
Springer, 2015. 231-240 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9474
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-272181DOI: 10.1007/978-3-319-27857-5_21ISI: 000376400300021ISBN: 9783319278568 (print)ISBN: 9783319278575 (print)OAI: oai:DiVA.org:uu-272181DiVA: diva2:893350
Conference
ISVC 2015, December 14–16, Las Vegas, NV
Projects
q2b
Funder
Swedish Research Council, 2012-5743
Available from: 2015-12-18 Created: 2016-01-12 Last updated: 2016-07-19Bibliographically approved

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Wilkinson, TomasBrun, Anders

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