Multilayer cluster neural network for totally unconstrained handwritten numeral recognition

In this paper, we propose a simple multilayer cluster neural network with five independent subnetworks for off-line recognition of totally unconstrained handwritten numerals. We also show that the use of genetic algorithms for avoiding the problem of finding local minima in training the multilayer cluster neural network with gradient descent technique reduces error rates. In order to verify the performance of the proposed multilayer cluster neural network, experiments with unconstrained handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. For the case of determining the initial weights of the multilayer cluster neural network randomly, the error rates were 2.90%, 1.50%, and 0.80%, respectively. And, for the case of determining the initial weights using a genetic algorithm, the error rates were 2.20%, 0.87%, and 0.60%, respectively.