Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/40736
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dc.coverage.spatialComputer Scienceen_US
dc.date.accessioned2015-05-09T08:28:33Z-
dc.date.available2015-05-09T08:28:33Z-
dc.date.issued2015-05-09-
dc.identifier.urihttp://hdl.handle.net/10603/40736-
dc.description.abstractTamil is one of the ancient languages which contain information about ancient history of India medicinal notes astrology and so on which have been written in ancient Tamil scripts which is different from current Tamil scripts If these inscriptions were digitized the contents available in them can be used by various categories of people with ease and comfort Hence this research work is carried out in order to classify the handwritten ancient Tamil scripts that facilitate the development of handwritten character recognition The main objective of this research work to find an optimal solution for the classification of handwritten Tamil scripts In order to achieve this the various tasks in image processing are carried out First a binarization algorithm is proposed using Otsu and Particle Swarm Optimization technique to convert the input samples into a binary image As the document image analysis methods are very sensitive to rotation the document skew should be corrected For skew detection and correction modified projection profile method is used This method corrects the document skew with minimum estimation error Using Particle swarm optimization technique the text lines are segmented from the document and to segment the characters a method combining connected components and nearest neighborhood method is used Then a new set of feature vectors is formed by extracting the features from the character segments using Zernike moments and regional features The features are fed to Extreme Learning Machine ELM and Complex Extreme Learning Machine CELM for newlineclassification ELM and CELM takes higher number of hidden neurons in order to classify the 11th century scripts Hence in order to obtain the highest accuracy for the classification with minimum number of hidden neurons a new method is proposed using Differential Evolution algorithm in the CELM The proposed method is tested on 11th century handwritten Tamil scripts It is observed that the proposed method achieves a higher classification rate when compared with other methodsen_US
dc.format.extenten_US
dc.languageEnglishen_US
dc.relation100en_US
dc.rightsuniversityen_US
dc.titleOptimized Complex Extreme Learning Machine for Classification of 11th Century Handwritten Tamil Scriptsen_US
dc.title.alternativeen_US
dc.creator.researcherSridevi Nen_US
dc.subject.keywordHandwritten Charactersen_US
dc.subject.keyword11th century Tamil Scriptsen_US
dc.subject.keywordELM, DE-CELMen_US
dc.description.noteen_US
dc.contributor.guideSubashini Pen_US
dc.publisher.placeCoimbatoreen_US
dc.publisher.universityAvinashilingam Deemed University For Womenen_US
dc.publisher.institutionDepartment of Computer Scienceen_US
dc.date.registered01/04/2010en_US
dc.date.completed01/04/2013en_US
dc.date.awarded15/04/2015en_US
dc.format.dimensions210 X 297mmen_US
dc.format.accompanyingmaterialCDen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Department of Computer Science

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nsridevi_chapter1.pdfAttached File482.26 kBAdobe PDFView/Open
nsridevi_chapter2.pdf296.78 kBAdobe PDFView/Open
nsridevi_chapter3.pdf434.88 kBAdobe PDFView/Open
nsridevi_chapter4.pdf615.48 kBAdobe PDFView/Open
nsridevi_chapter5.pdf757.67 kBAdobe PDFView/Open
nsridevi_chapter6.pdf260.43 kBAdobe PDFView/Open
nsridevi_chapter7.pdf2.73 MBAdobe PDFView/Open
nsridevi_intro.pdf240.63 kBAdobe PDFView/Open


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