Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/13335
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dc.contributor.authorLow, Siew Eng.en_US
dc.date.accessioned2008-10-20T07:25:24Z-
dc.date.available2008-10-20T07:25:24Z-
dc.date.copyright1998en_US
dc.date.issued1998en_US
dc.identifier.urihttp://hdl.handle.net/10356/13335-
dc.description.abstractIn this project, two learning schemes had been tested and verified, in the recognition unit within the image processing environment of the Machine Vision Tool, namely the Bayes classifier method and the neural network-error back propagation gradient descent learning method. Their performances were analysed and compared. Sometimes the algorithms, especially the Bayes classifier method, fail to distinguish between two characters that are inherently similar, such as 'S" and '5', 'B' and '8'.en_US
dc.format.extent130 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processingen_US
dc.titleComparison of character recognition performance - bayes classifier and neural network methodsen_US
dc.typeThesisen_US
dc.contributor.supervisorChan, Kap Luken_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
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