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Volume & Issue no: Volume 3, Issue 6, June 2014


Neuro-Fuzzy Model Based Classification of Handwritten Hindi Modifiers
Author Name:
Gunjan Singh, Dr. Avinash Pokhriyal, Prof. Sushma Lehri
Abstract Automatic character recognition is one of the most important and interesting topic of pattern recognition field. A lot of work has been done in the area of machine printed character recognition, but recognition of handwritten characters is comparatively difficult and still a subject of active research. In this paper, we present a neuro-fuzzy system for accurate and adequate classification of handwritten Hindi modifiers or matras. Total 1000 handwritten samples of 10 modifiers are collected from 10 different people. System works in six stages—data collection & scanning, preprocessing, normalization, feature extraction, fuzzy rule set creation and classification. System works on fuzzy information and has a layered architecture—input layer, hidden layers and output layer. Function of first hidden or degree of membership (DOM) layer is to generate the degree of membership for all input-output pattern pairs. Generated degree of membership is then used to train the system using backpropagation algorithm to perform final classification of rules and to determine the membership function of generated output for each class. The membership function is used to determine the classified output pattern corresponding to the input pattern. Feature extraction is done by convoluting a 3X3 mask on pre-processed modifier image. At the end, a comparative study of proposed system with existing systems is also done to evaluate the performance. The proposed system has been implemented in MATLAB 2009 environment. Keywords: Neuro-fuzzy model, classification, fuzzy if-then rules, membership function, backpropagation learning algorithm
Cite this article:
Gunjan Singh, Dr. Avinash Pokhriyal, Prof. Sushma Lehri , " Neuro-Fuzzy Model Based Classification of Handwritten Hindi Modifiers" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 3, Issue 6, June 2014 , pp. 311-325 , ISSN 2319 - 4847.
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