Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2010-05-21
ICIIS 2008
Computer Science
Computer Vision and Pattern Recognition
6 pages, 8-10 December 2008
Scientific paper
In this paper we present an OCR for Handwritten Devnagari Characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain code histogram features and line fitting features are computed by dividing the character image into different segments. Weighted majority voting technique is used for combining the classification decision obtained from four Multi Layer Perceptron(MLP) based classifier. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.80% as we considered top five choices results. This method is compared with other recent methods for Handwritten Devnagari Character Recognition and it has been observed that this approach has better success rate than other methods.
Arora Sandhya
Basu Dipak Kumar
Bhattacharjee Debotosh
Kundu Mahantapas
Nasipuri Mita
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