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Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/2884
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dc.contributor.authorShelke, S. V.-
dc.contributor.authorChandwadkar, D. M.-
dc.date.accessioned2020-12-17T07:34:07Z-
dc.date.available2020-12-17T07:34:07Z-
dc.date.issued2016-04-01-
dc.identifier.urihttp://192.168.3.232:8080/jspui/handle/123456789/2884-
dc.description.abstractRecognition of handwritten characters has been a popular research area for many years. Devnagari script is a major script of India and is widely used for various languages. In this paper we propose a system to recognize devnagri handwritten characters. Total 60 devnagri characters (50 letters and 10 digits) are taken in to consideration. 60 samples of each character i.e. total 3600 samples are used for features extraction. Classification is done by four different classifiers which are Multilayer perceptron, K-Nearest Neighbour, Naive Bayes classifier and Classification tree. Performance of different classifiers is compared.98.9 % accuracy is obtained by Multilayer perceptronen_US
dc.subjectDevnagri Charactersen_US
dc.subjectNaive Bayesen_US
dc.subjectMultilayer perceptronen_US
dc.subjectK-Nearest Neighbouren_US
dc.titleHandwritten Devnagri Character Recognitionen_US
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