Class-adaptive zoning methods for recognizing handwritten digits and characters

This paper presents a new approach for zoning design based on a class-adaptive technique in which the optimal zoning method is defined for each class. For this purpose, in the zoning design stage, a multi-objective genetic algorithm was used to determine, for each class, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests were carried out in the field of handwritten digit and character recognition. The results show that the new class-adaptive zoning methods proposed in this paper are superior to the set-adaptive methods presented in the literature.