A binarization feature extraction approach to OCR: MLP vs. RBF

Abstract

The aim of this work is to judge the efficiency of Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural network classifiers for performing the task of cursive handwritten digit recognition. Binarization features are extracted from the preprocessed handwritten digit images. The features thus obtained are used to train MLP and RBF classifiers. A detailed investigation in the proposed experiment was done and it can be summarized that when binarization features of the digit images are extracted and used for training the neural network classifiers in the recognition experiment, RBF classifier outperforms the MLP classifier. The RBF Network delivers 98.40% recognition accuracy whereas the MLP classifier delivers 96.20% accuracy for the proposed experiment of cursive handwritten digit recognition. © 2014 Springer International Publishing Switzerland.