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Embedded Prototype Subspace Classification: A subspace learning framework
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, Computerized Image Analysis and Human-Computer 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
2019 (English)In: Computer Analysis of Images and Patterns, CAIP 2019, PT II, Springer, 2019, p. 581-592Conference paper, Published paper (Refereed)
Abstract [en]

Handwritten text recognition is a daunting task, due to complex characteristics of handwritten letters. Deep learning based methods have achieved significant advances in recognizing challenging handwritten texts because of its ability to learn and accurately classify intricate patterns. However, there are some limitations of deep learning, such as lack of well-defined mathematical model, black-box learning mechanism, etc., which pose challenges. This paper aims at going beyond the blackbox learning and proposes a novel learning framework called as Embedded Prototype Subspace Classification, that is based on the well-known subspace method, to recognise handwritten letters in a fast and efficient manner. The effectiveness of the proposed framework is empirically evaluated on popular datasets using standard evaluation measures.

Place, publisher, year, edition, pages
Springer, 2019. p. 581-592
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11679
Keywords [en]
Handwritten text, Subspaces, Deep learning, t-SNE
National Category
Medical Image Processing Human Computer Interaction
Identifiers
URN: urn:nbn:se:uu:diva-393257DOI: 10.1007/978-3-030-29891-3_51ISI: 000558110900051ISBN: 978-3-030-29891-3 (electronic)ISBN: 978-3-030-29890-6 (print)OAI: oai:DiVA.org:uu-393257DiVA, id: diva2:1352303
Conference
The 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, September 2–6,2019, Salerno, Italy
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2020-10-27Bibliographically approved

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Publisher's full texthttps://caip2019.unisa.it/

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Hast, AndersLind, MatsVats, Ekta

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CiteExportLink to record
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  • apa
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Output format
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  • asciidoc
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