Quantcast

Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges

Research paper by Jayashree Rajesh Prasad, Uday Kulkarni

Indexed on: 24 May '14Published on: 24 May '14Published in: International Journal of Machine Learning and Cybernetics



Abstract

Recognition of Indian scripts is a challenging problem and work towards development of an OCR for handwritten Gujarati, an Indian script is still in infancy. This paper implements an Adaptive Neuro Fuzzy Classifier (ANFC) for Gujarati character recognition using fuzzy hedges (FHs). FHs are trained with other network parameters by scaled conjugate gradient training algorithm. The tuned fuzzy hedge values of fuzzy sets improve the flexibility of fuzzy sets; this property of FH improves the distinguishability rates of overlapped classes. This work is further extended for feature selection based on FHs. The values of fuzzy hedges can be used to show the importance of degree of fuzzy sets. According to the FH value, the redundant, noisily features can be eliminated, and significant features can be selected. An FH-based feature selection algorithm is implemented using ANFC. This paper aims to demonstrate recognition of ANFC-FH and improved results of the same with feature selection.