Gujrati character recognition using weighted k-NN and Mean ? 2 distance measure
With advances in the field of digitization, document analysis and handwriting recognition have emerged as key research areas. Authors present a handwritten character recognition system for Gujrati, an Indian language spoken by 40million people. The proposed system extracts four features. A unique pattern descriptor and Gabor phase XNOR pattern are the two features that are newly proposed for isolated handwritten character set of Gujrati. In addition to these two features, we use contour direction probability distribution function and autocorrelation features. Next contribution is the weighted k-NN classifier. This research finally contributes is a novel mean ? 2 distance measure. Proposed classifier exploits a combination of feature weights, new distance measure along with a triangular distance and Euclidian distance for performance that improves conventional k-NN classifier. The implementation on a comprehensive data set show 86.33% recognition efficiency. Facts and figures show that proposed approach outperforms conventional k-NN. It is concluded that despite the shape ambiguities in Indian scripts, proposed classification algorithm could be a dominant technique in the field of handwritten character recognition.
optical character recognition (ocr) weighted k-nn classification gabor phaser xnor pattern