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100   $a20160829d2007      uy 
101 0 $aeng
135   $aur|||||||||||
181   $ctxt
182   $cc
183   $acr
200 10$aMachine Learning and Applications; Proceedings: International Conference on Machine Learning and Applications (6th: 2007: Cincinnati, Ohio)
210 31$a[Place of publication not identified]$cIEEE Computer Society Press$d2007
215   $a1 online resource
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a9780769530697 
311 08$a0769530699 
330   $aAn optical character recognition (OCR) system with a high recognition rate is challenging to develop. One of the major contributors to OCR errors is smeared characters. Several factors lead to the smearing of characters such as bad scanning quality and a poor binarization technique. Typical approaches to character segmentation falls into three major categories: image-based, recognition-based, and holistic-based. Among these approaches, the segmentation path can be linear or non-linear. Our paper proposes a non-linear approach to segment characters on grayscale document images. Our method first determines whether characters are smeared together using general character features. The correct segmentation path is found using a shortest path approach. We achieved a segmentation accuracy of 95% over a set of about 2,000 smeared characters.
606   $aMachine learning$vCongresses
615  0$aMachine learning
676   $a006.31
700   $aWani$b M. Arif$01006177
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906   $aPROCEEDING
912   $a9910145148603321
996   $aMachine Learning and Applications; Proceedings: International Conference on Machine Learning and Applications (6th: 2007: Cincinnati, Ohio)$92314962
997   $aUNINA