FUZZY RULE BASED CLASSIFICATION AND RECOGNITION OF HANDWRITTEN HINDI CURVE SCRIPT
Gunjan Singh, Avinash Pokhriyal, Sushma Lehri · 2013
This paper presents a novel system for classification and recognition of handwritten Hindi script using fuzzy rule based approach. Classification & recognition of handwritten Hindi script is a complex task as characters are cursive in nature and demonstrate a lot of similar features. The quality of fuzzy logic to deal with vague and imprecise data makes it appropriate for such problems. In this paper, we focus on two or three letter words without modifiers. Prior to recognition, handwritten words are preprocessed and segmented into individual characters. The performance of an optical character recognition system extremely depends on the procedure used to extract quality features from characters. During classification stage characters are classified into seven classes using fuzzy if-then rules based on one of the most important component of Hindi characters – the vertical bar. Features such as curves, lines, junction points and endpoints are used at the recognition stage. A 3x3 mask is used to extract features from character image. System was tested for total 450 words written by 30 different people. Experimental results show that the proposed method performs classification and recognition at the rate of 92.02%. The proposed system has been implemented in MATLAB 2009 environment.