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Please use this identifier to cite or link to this item: http://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3695
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dc.contributor.authorDhandra B.V
dc.contributor.authorBenne R.G
dc.contributor.authorHangarge M.
dc.date.accessioned2020-06-12T15:01:07Z-
dc.date.available2020-06-12T15:01:07Z-
dc.date.issued2008
dc.identifier.citationProceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2007 , Vol. 2 , , p. 224 - 228en_US
dc.identifier.uri10.1109/ICCIMA.2007.219
dc.identifier.urihttp://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3695-
dc.description.abstractThis paper deals with the automatic recognition of handwritten Isolated Kannada numerals based on structural features. Four different types of structural features namely, directional density of pixels in four directions, water reservoirs, maximum profile distances, and fill hole density are used for the recognition of numerals. A Minkowski minimum distance criteria is used to find minimum distances and K-nearest neighbor classifier is used to classify the Kannada numerals. A total 1512 numeral images are tested, and the overall accuracy is found to be 96.12%. The novelty of the proposed method is that it is thinning free, fast and writer style independent. © 2007 IEEE.en_US
dc.titleHandwritten Kannada numeral recognition based on structural featuresen_US
dc.typeConference Paper
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