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Multilayer perceptrons combination applied to handwritten character recognition

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Abstract

Several methods of combination of Multilayer Perceptrons (MLPs) for handwritten character recognition are presented and discussed. Recognition tests have shown that cooperation of neural networks using different features vectors can reduce significantly the overall misclassification error rate. Additionally, the MLPs that are combined are the results of the experiments that were previously performed in order to optimize the recognition process when using a single MLP. So, all the combination methods that are proposed are very easy to carry out. The final recognition system consists of a cascade association of small MLPs, which allows minimization of the overall recognition time while retaining a high recognition rate. This system appears to be 2.5 times faster than the best of the individual MLPs, while offering a recognition rate of 99.8% on unconstrained digits extracted from the NIST 3 database.

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  1. Circuit Theory & Signal Processing Laboratory, Faculté Polytechnique de Mons, Bd Dolez 31, B-7000, Mons, Belgium

    Bernard Gosselin

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  1. Bernard Gosselin
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Gosselin, B. Multilayer perceptrons combination applied to handwritten character recognition. Neural Process Lett 3, 3–10 (1996). https://doi.org/10.1007/BF00417783

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