Handwritten Digit Recognition by Multi-objective Optimization of Zoning Methods

Author

Impedovo, S. ; Pirlo, G. ; Mangini, F.M.

Author_Institution

Dipt. di Inf., Univ. degli Studi di Bari A. Moro, Bari, Italy

fYear

2012

fDate

18-20 Sept. 2012

Firstpage

675

Lastpage

679

Abstract

This paper addresses the use of multi-objective optimization techniques for optimal zoning design in the context of handwritten digit recognition. More precisely, the Non-dominant Sorting Genetic Algorithm II (NSGA II) has been considered for the optimization of Voronoi-based zoning methods. In this case both the number of zones and the zone position and shape are optimized in a unique genetic procedure. The experimental results point out the usefulness of multi-objective genetic algorithms for achieving effective zoning topologies for handwritten digit recognition.

Keywords

genetic algorithms; handwritten character recognition; NSGA 2; Voronoi-based zoning method; handwritten digit recognition; multiobjective optimization; nondominant sorting genetic algorithm 2; zone position; zone shape; zoning topology; Genetic algorithms; Handwriting recognition; Optimization; Sociology; Sorting; Statistics; Topology; Genetic Algorithms; Handwritten Digit Recognition; NSGA II; Voronoi Diagrams; Zoning Topology;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on

Conference_Location

Bari

Print_ISBN

978-1-4673-2262-1

Type

conf

DOI

10.1109/ICFHR.2012.209

Filename

6424474