26 February 2010 Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters
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Proceedings Volume 7546, Second International Conference on Digital Image Processing; 754633 (2010) https://doi.org/10.1117/12.853479
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore
Abstract
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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Rahul Rishi, Amit Choudhary, Ravinder Singh, Vijaypal Singh Dhaka, Savita Ahlawat, Mukta Rao, "Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754633 (26 February 2010); doi: 10.1117/12.853479; https://doi.org/10.1117/12.853479
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