A hybrid feature and discriminant classifier for high accuracy handwritten Odia numeral recognition

Unconstrained handwritten character recognition is a major research area where there is a lot of scope for improving accuracy. There are many statistical, structural feature extraction techniques being proposed for different languages. Many classifier models are combined with these features to obtain high recognition rates. There still exists a gap between the recognition accuracy of printed characters and unconstrained handwritten scripts. Odia is a popular and classical language of the eastern part of India. Though the research in Optical Character Recognition (OCR) has advanced in other Indian languages such as Devanagari and Bangla, not much attention has been given to Odia character recognition. We propose a hybrid feature extraction technique using Kirsch gradient operator and curvature properties of handwritten numerals, followed by a feature dimension reduction using Principal Component Analysis (PCA). We use Modified Quadratic Discriminant Function (MQDF), Discriminative Learning Quadratic Discriminant Function (DLQDF) classifiers as they provide high accuracy of recognition and compare both the classifier performances. We verify our results using the Odia numerals database of ISI Kolkata. The recognition accuracy for Odia numerals with our proposed approach is found to be 98.5%.