Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters
Rahul Rishi, Amit Kumar Choudhary, Ravinder Singh, Vijaypal Singh Dhaka, Savita Ahlawat, Mukta Rao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
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.