Discrete Cosine transform-based feature selection for marathi numeral recognition system

Abstract

Optical character recognition system is hotcake for the researchers since last four decades. Recognition of handwritten Devanagari characters and digits is comparatively a tough task as compared to recognition other scripts like English or Latin. In this manuscript, a novel feature extraction and selection method is proposed for the recognition of isolated handwritten Marathi numbers based on one-dimensional Discrete Cosine Transform (1-D DCT) algorithm for reducing the dimensionality of feature space. The scanned document is preprocessed and segmented to create isolated numerals. Features for each numeral can be calculated after normalizing the numeral image to 32 × 32 size. Based on these reduced features, the numerals are classified into appropriate groups. Database of 6000 numerals size is used for the proposed work. Neural network is used for classification of numerals based on the extracted and selected features. Experimental results show accuracy observed for the method is 90.30%.