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Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2017 (English)In: 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016, Institute of Electrical and Electronics Engineers Inc. , 2017, 305-310 p., 7886054Conference paper (Refereed)
Abstract [en]

In this paper, a new approach is proposed to enhance the handwriting image by using learning-based windowing contrast enhancement and Gaussian Mixture Model (GMM). A fixed size window moves over the handwriting image and two quantitative methods which are discrete entropy (DE) and edge-based contrast measure (EBCM) are used to estimate the quality of each patch. The obtained results are used in the unsupervised learning method by using k-means clustering to assign the quality of handwriting as bad (if it is low contrast) or good (if it is high contrast). After that, if the corresponding patch is estimated as low contrast, a contrast enhancement method is applied to the window to enhance the handwriting. GMM is used as a final step to smoothly exchange information between original and enhanced images to discard the artifacts to represent the final image. The proposed method has been compared with the other contrast enhancement methods for different datasets which are Swedish historical documents, DIBCO2010, DIBCO2012 and DIBCO2013. Results illustrate that proposed method performs well to enhance the handwriting comparing to the existing contrast enhancement methods. © 2016 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. 305-310 p., 7886054
Keyword [en]
contrast enhancement, Gaussian mixture modeling, Handwriting image enhancement, k-means clustering, learning-based windowing, Gaussian distribution, Image enhancement, Image segmentation, Unsupervised learning, Discrete entropy, Gaussian Mixture Model, Historical documents, Quantitative method, Unsupervised learning method, Signal processing
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-14146DOI: 10.1109/ISSPIT.2016.7886054ScopusID: 2-s2.0-85017608194ISBN: 9781509058440 OAI: oai:DiVA.org:bth-14146DiVA: diva2:1092893
Conference
2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016, Limassol
Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-05-04Bibliographically approved

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Kusetogullari, HüseyinGrahn, HåkanLavesson, Niklas
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
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Output format
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