NDLI: Self Learning Classification for Degraded Document Images by Sparse Representation

Content Provider	IEEE Xplore Digital Library
Author	Bolan Su Shuangxuan Tian Shijian Lu Thien Anh Dinh Chew Lim Tan
Copyright Year	2013
Description	Author affiliation: Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore (Bolan Su; Shuangxuan Tian; Thien Anh Dinh; Chew Lim Tan) \|\| Inst. for Infocomm Res., Singapore, Singapore (Shijian Lu)
Abstract	Document Image Binarization is a technique to segment text out from the background region of a document image, which is a challenging task due to high intensity variations of the document foreground and background. Recently, a series of document image binarization contests (DIBCOs) had been held that have drawn great research interest in this area. Several document binarization techniques have been proposed and achieve great performance on the contest datasets. However, those proposed techniques may not perform well on all kinds of degraded document images because it is difficult to design a classification method that correctly models the non-uniform degraded document background and text foreground simultaneously. In this paper, we propose a self learning classification framework that combines binary outputs of different binarization methods. The proposed framework makes used of the sparse representation to re-classify the document pixels and produces a better binary results. The experimental results on the recent DIBCO contests show the great performance and robustness of our proposed framework on different kinds of degraded document images.
Starting Page	155
Ending Page	159
File Size	272467
Page Count	5
File Format	PDF
ISBN	9780769549996
ISSN	15205363
DOI	10.1109/ICDAR.2013.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Feature extraction Text analysis Robustness Testing Training Equations Document Image Binarization Sparse Representation Self Learning Classification
Content Type	Text
Resource Type	Article

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