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Unsupervised Text Binarization in Handwritten Historical Documents Using k-Means Clustering
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Inst Technol, Dept Comp Sci & Engn, S-37141 Karlskrona, Sweden..ORCID iD: 0000-0001-7536-3349
2018 (English)In: PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 2 / [ed] Bi, Y Kapoor, S Bhatia, R, SPRINGER INTERNATIONAL PUBLISHING AG , 2018, p. 23-32Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a novel technique for unsupervised text binarization in handwritten historical documents using k-means clustering. In the text binarization problem, there are many challenges such as noise, faint characters and bleed-through and it is necessary to overcome these tasks to increase the correct detection rate. To overcome these problems, preprocessing strategy is first used to enhance the contrast to improve faint characters and Gaussian Mixture Model (GMM) is used to ignore the noise and other artifacts in the handwritten historical documents. After that, the enhanced image is normalized which will be used in the postprocessing part of the proposed method. The handwritten binarization image is achieved by partitioning the normalized pixel values of the handwritten image into two clusters using k-means clustering with k = 2 and then assigning each normalized pixel to the one of the two clusters by using the minimum Euclidean distance between the normalized pixels intensity and mean normalized pixel value of the clusters. Experimental results verify the effectiveness of the proposed approach.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG , 2018. p. 23-32
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370 ; 16
Keywords [en]
Handwritten text binarization, Image processing, k-means clustering, Document images
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-17280DOI: 10.1007/978-3-319-56991-8_3ISI: 000448662500003ISBN: 978-3-319-56991-8 OAI: oai:DiVA.org:bth-17280DiVA, id: diva2:1263360
Conference
SAI Annual Conference on Areas of Intelligent Systems and Artificial Intelligence and their Applications to the Real World (IntelliSys), SEP 21-22, 2016, London, ENGLAND
Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2018-11-15

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Kusetogullari, Hüseyin

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7891011121310 of 28
CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
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