In this work we examine the applicability of an evolutionary algorithm to the problem of optical character recognition. Classification technique is template matching and minimum weighted error. This kind of problem can be turned into an optimisation problem. In particular, we concentrate segmentation and classification on Genocop III algorithm, proposed by Michalewicz [1] for numerical optimisation for constrained problems using a multi-objective function. The proposed algorithm shows good performances and accuracy.

A Character Recognition Handling Constraints Genetic Algorithm: The License Plate Case Study

V. Bevilacqua;G. Mastronardi;
2004

Abstract

In this work we examine the applicability of an evolutionary algorithm to the problem of optical character recognition. Classification technique is template matching and minimum weighted error. This kind of problem can be turned into an optimisation problem. In particular, we concentrate segmentation and classification on Genocop III algorithm, proposed by Michalewicz [1] for numerical optimisation for constrained problems using a multi-objective function. The proposed algorithm shows good performances and accuracy.
IASTED International Conference on Artificial Intelligence and Applications, AIA-2004
0-88986-404-7
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/14170
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