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- W2188089283 abstract "Separation ofprinted textblocks fromthenon-text areas, containing signatures, handwritten text, logos andother such symbols, isanecessary first step foranOCRinvolving printed text recognition. Inthepresent work, wecompare theefficacy ofsomefeature-classifier combinations tocarry outthis separation task. Wehaveselected length-normalized horizontal projection profile (HPP)asthestarting point ofsuchaseparation task. Thisiswiththeassumption that theprinted text blocks contain lines oftext whichgenerate HPP'swithsome regularity. Suchanassumption isdemonstrated tobevalid. Ourfeatures aretheHPP anditstwotransformed versions, namely, eigen andFisher profiles. Fourwell knownclassifiers, namely, Nearest neighbor, Linear discriminant function, SVM's andartificial neural networks havebeenconsidered and efficiency ofthecombination ofthese classifiers withtheabove features iscompared. A sequential floating feature selection technique hasbeenadopted toenhance theefficiency ofthis separation task. Theresults give anaverage accuracy ofabout 96%." @default.
- W2188089283 created "2016-06-24" @default.
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- W2188089283 date "2006-01-01" @default.
- W2188089283 modified "2023-09-27" @default.
- W2188089283 title "Automatic textblock separation indocumentimages" @default.
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