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Ornamental letters image classification using local dissimilarity maps
Univ. of Reims-Champagne-Ardenne - CReSTIC, IUT, Troyes Cedex, France.ORCID iD: 0000-0002-9999-9197
Univ. of Reims-Champagne-Ardenne - CReSTIC, IUT, Troyes Cedex, France.
Univ. of Reims-Champagne-Ardenne - CReSTIC, IUT, Troyes Cedex, France.
2009 (English)In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, IEEE, 2009, p. 186-190Conference paper, Published paper (Refereed)
Abstract [en]

This article describes a new method for ancient books ornamental letters segmentation and recognition. The purpose of our work is to automatically determine the letter represented in an ornamental letter image. Our process is divided in two parts: a segmentation step of the ornamental letter is followed by a recognition step. The segmentation process uses multiresolution analysis to filter background decorations followed by a binarisation step and a morphologic reconstruction of the expected letter. The recognition process use the previously obtained reconstruction and compares it with capital letters images used as a dictionary of shapes with the Local Dissimilarity Map (LDM) distance.

Place, publisher, year, edition, pages
IEEE, 2009. p. 186-190
Keywords [en]
Binarisation, Recognition process, Segmentation process, Image analysis, Image classification, Image reconstruction, Multiresolution analysis
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:hj:diva-60439DOI: 10.1109/ICDAR.2009.226Scopus ID: 2-s2.0-71249096922ISBN: 9780769537252 (print)OAI: oai:DiVA.org:hj-60439DiVA, id: diva2:1759109
Conference
ICDAR2009 - 10th International Conference on Document Analysis and Recognition, 26 July 2009 through 29 July 2009, Barcelona
Available from: 2023-05-24 Created: 2023-05-24 Last updated: 2023-05-24Bibliographically approved

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Landré, Jérôme

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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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
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  • text
  • asciidoc
  • rtf