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Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
2017 (English)In: 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 305-310, article id 7886054Conference paper, Published 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proposed method has been compared with the other contrast enhancement methods for different datasets which are Swedish historical documents, DIBCO2010, DIBCO2012 and DIBCO2013.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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. p. 305-310, article id 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distribution, Image enhancement, Image segmentation, Unsupervised learning, Discrete entropy, Gaussian Mixture Model, Historical documents, Quantitative method, Unsupervised learning method, Signal processing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 Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37937DOI: 10.1109/ISSPIT.2016.7886054ISI: 000406122500056Scopus ID: 2-s2.0-85017608194ISBN: 9781509058440 (print)OAI: oai:DiVA.org:hj-37937DiVA, id: diva2:1159948
Conference
2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016, Limassol
Funder
Knowledge Foundation, 20140032]
Available from: 2017-05-04 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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Lavesson, Niklas

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  • apa
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