In this paper we propose a system for classification problem of handwritten text. The system is composed of
preprocessing module, supervised learning module and recognition module on a very broad level. The
preprocessing module digitizes the documents and extracts features (tangent values) for each character. The
radial basis function network is used in the learning and recognition modules. The objective is to analyze and
improve the performance of Multi Layer Perceptron (MLP) using RBF transfer functions over Logarithmic
Sigmoid Function. The results of 35 experiments indicate that the Feed Forward MLP performs accurately and
exhaustively with RBF. With the change in weight update mechanism and feature-drawn preprocessing module,
the proposed system is competent with good recognition show.
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