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Volume: 24 | Article ID: art00046_2
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Projection-based Scanned Image Enhancement
Mohamed Nooman Ahmed
  DOI :  10.2352/ISSN.2169-4451.2008.24.1.art00046_2  Published Online :  January 2008
Abstract

In this paper, we present a new system to segment and label document images by combining statistical and multiscale view of different image components. Texture of text, halftone and images are characterized by modeling the distribution of a novel intensity projection technique using a mixture of K Gaussians. Model parameters are then estimated using the expectation maximization (EM) algorithm. Using the proposed algorithm, halftone areas were successfully differentiated from text regions

Journal Title : NIP & Digital Fabrication Conference
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
Subject Areas :
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 DOI 10.2352/ISSN.2169-4451.2008.24.1.art00046_2
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Mohamed Nooman Ahmed, "Projection-based Scanned Image Enhancement"  in Proc. IS&T Int'l Conf. on Digital Printing Technologies and Digital Fabrication (NIP24),  2008,  pp 635 - 638,  https://doi.org/10.2352/ISSN.2169-4451.2008.24.1.art00046_2

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Copyright © Society for Imaging Science and Technology 2008
72010410
NIP & Digital Fabrication Conference
nip digi fabric conf
2169-4451
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA
2169-4451(20080101)2008:2L.635;1-
nip_v2008n2/splitsection46.xml
/ist/nipdf/2008/00002008/00000002/art00046
Articles
Projection-based Scanned Image Enhancement
AhmedMohamed Nooman
01012008
2008
2
635
638
2008
In this paper, we present a new system to segment and label document images by combining statistical and multiscale view of different image components. Texture of text, halftone and images are characterized by modeling the distribution of a novel intensity projection technique using a mixture of K Gaussians. Model parameters are then estimated using the expectation maximization (EM) algorithm. Using the proposed algorithm, halftone areas were successfully differentiated from text regions