Offline Handwritten Hindi 'SWARs' Recognition Using A Novel Wave Based Feature Extraction Method

Abstract

Offline handwritten character recognition is a very challenging area of research as handwriting of two persons may bear resemblance whereas handwriting of an individual may vary at different times. The character recognition accuracy depends on the ways the features are extracted from the samples and utilized to formulate the feature vector. In this paper, a novel technique 'TARANG' for feature extraction is proposed to recognize offline handwritten Hindi 'SWARs' (vowels). This technique for extracting features from an image is inspired by the natural movement of wave in a medium. A feature vector obtained by using proposed technique is used for the training of Backpropagation Neural Network and recognition rate as high as 96.2% is achieved.