PERFORMANCE EVALUATION OF MLP AND RBF CLASSIFIERS FOR HANDWRITTEN CHARACTER RECOGNITION USING HYBRID FEATURES

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Published: Aug 28, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i7.4539
Keywords:
OCR, MLP, RBF, Feature Extraction, Projection Profile Features, Hybrid Features

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Amit Choudhary
Maharaja Surajmal Institute
Vinod Kumar

Abstract

For evaluating the accuracy and capability of MLP (Multi Layer Perceptron) and RBF (Radial Basis Function) classifier algorithms; projection profile features for the character images are extracted and are merged with the binarization features after preprocessing every character image, and the combined features thus obtained are used to train both the classifiers (MLP and RBF Network) selected for performing character recognition task. Simulation studies are examined extensively and the proposed hybrid features are found to deliver the recognition accuracy of 93.46% when used with RBF Network as a classifier which is better than the 88.76% accuracy obtained when used with MLP Network as a classifier.

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Vol. 8 No. 7 (2017): July-August 2017
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