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Implementation of handwritten text recognition using density value of Delauney tessellation
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This paper presents a novel Word spotting technique for handwritten documentsusing density value of Delaunay triangulation. Delaunay tessellation is constructedfrom a set of data points on a query image and the density value is computed for eachdata point. This information is either directly used for training in a feed-forward neural network or used to compute the probability estimates of a class from Delaunay Tessellation Field Estimation and classification follows using naive Bayesian classifier. This paper discusses the performance of a Delaunay tessellation fieldestimation model and neural network model.

Place, publisher, year, edition, pages
2017. , p. 54
Series
IT ; 17070
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-333871OAI: oai:DiVA.org:uu-333871DiVA, id: diva2:1158122
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2017-11-21 Created: 2017-11-17 Last updated: 2017-11-21Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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