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Proceedings Paper

Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters
Author(s): Rahul Rishi; Amit Choudhary; Ravinder Singh; Vijaypal Singh Dhaka; Savita Ahlawat; Mukta Rao
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Paper 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.

Paper Details

Date Published: 26 February 2010
PDF: 7 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754633 (26 February 2010); doi: 10.1117/12.853479
Show Author Affiliations
Rahul Rishi, Technical Institute of Textile & Sciences Bhiwani (India)
Amit Choudhary, Maharaja Surajmal Institute (India)
Ravinder Singh, Maharaja Surajmal Institute (India)
Vijaypal Singh Dhaka, Institute of Management Studies (India)
Savita Ahlawat, Maharaja Surajmal Institute of Technology (India)
Mukta Rao, Interglobe Technologies, Ltd. (India)


Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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