A comparison of supervised classification methods for a statistical set of features: Application: Amazigh OCR

This paper is devoted to the study of supervised learning methods as part of pattern recognition and especially the Amazigh Characters Recognition. The goal is to compare a partial list of the popular automatic classification methods, and test the performance of the proposed features set extracted from isolated characters using statistical methods with these different classifiers.

In Experimental evaluation, several runs have been conducted for the different algorithms and the best accuracy observed is for the multilayer perceptron with a recognition rate about 96,47% which is very satisfactory.

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