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In this paper, a new classier combination model is pre-sented for Farsi handwritten digit recognition. The model is consistedof four RBF neural networks as the experts and another RBF networkas the gating network which learns to split the input space betweenthe experts. Considering the input data, which is an 81-element vectorextracted using the loci characterization method, the gating networkassigns a competence coecient to each expert. The nal output iscomputed as the weighted sum of the outputs of the experts. Therecognition rate of the proposed model is 93.5% which is 3.75% morethan the rate of the mixture of MLPs experts previously ran on thesame database.Keywords: mixture of experts, handwritten digit recognition, locicharacterization methodClassication: Science and engineering for electronics References[1] I. D. Trier and A. K. Jain, “Feature extraction Methods for CharacterRecognition- A Survey,” Pattern Recognition , vol. 29, no. 4, pp. 641–662,1996.[2] L. I. Kuncheva, M. Skurichina, and R. P. W. Duin, “An experimentalStudy on diversity for bagging and boosting with linear classiers,”

Farsi handwritten digit recognition based on mixture of RBF experts