Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges
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ORIGINAL ARTICLE
Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges Jayashree Rajesh Prasad • Uday Kulkarni
Received: 2 August 2013 / Accepted: 17 April 2014 Ó Springer-Verlag Berlin Heidelberg 2014
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 FHbased feature selection algorithm is implemented using ANFC. This paper aims to demonstrate recognition of ANFCFH and improved results of the same with feature selection. Keywords Concentration Dilution Feature selection (FS) Fuzzy Hedges (FHs) Fuzzy surface transformers
1 Introduction India is a land of many languages and Gujarati is an Indic script similar in appearance to other Indo-Aryan scripts. J. R. Prasad (&) Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India e-mail: [email protected] U. Kulkarni Department of Computer Engineering, SGGS Institute of Engineering and Technology, Nanded, India e-mail: [email protected]
Gujarati script has a rich literary heritage. However, research in the field of Gujarati script recognition faces major problems mainly due to a large set of visually similar characters, multi-component characters, touching and broken characters. This paper presents a pattern recognition system for Gujarati character recognition. Authors use combination of four features. Novel Gabor phase XNOR pattern (GPXNP) and pattern descriptor are proposed for isolated handwritten character set of Gujarati. In addition to these two features, authors use Contour Direction Probability Distribution Function (CDPDF) and autocorrelation features. Furthermore, authors present the design and development of an ANFC for recognition of isolated handwritten characters of Gujarati. Authors exploit the method of employing adaptive networks to solve a fuzzy classification problem. System parameters, such as the membership functions (MFs) defined for each feature and the parameterized t-norms used to combine conjunctive conditions are calibrated with backpropagation. This paper is organized as follows: Sect. 2 surveys related work. Motivation behind this research is presented in Sect. 3. Section 4 justifies significance of present work on Gujarati script and focuses on challenges and opportunities in the resea
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