NDLI: A learning framework for degraded document image binarization using Markov Random Field

Content Provider	IEEE Xplore Digital Library
Author	Bolan Su Shijian Lu Chew Lim Tan
Copyright Year	2012
Description	Author affiliation: Department of Computer Vision and Image Understanding, Institute for Infocomm Research 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632 (Shijian Lu) \|\| Department of Computer Science, School of Computing, National University of Singapore Computing 1, 13 Computing Drive, Singapore 117417 (Bolan Su; Chew Lim Tan)
Abstract	Document image binarization is an important preprocessing technique for document image analysis that segments the text from the document image backgrounds. Many techniques have been proposed and successfully applied in different applications, such as document image retrieval. However, these techniques may perform poorly on degraded document images. In this paper, we propose a learning framework that makes use of the Markov Random Field to improve the performance of the existing document image binarization methods for those degraded document images. Extensive experiments on the recent Document Image Bina-rization Contest datasets demonstrate that significant improvements of the existing binarization methods when applying our proposed framework.
Starting Page	3200
Ending Page	3203
File Size	896837
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Image edge detection Markov random fields Vectors Equations Mathematical model Text analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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