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Writer Identification Using Modular MLP Classifier and Genetic Algorithm for Optimal Features Selection

  • Sami Gazzah
  • Najoua Essoukri Ben Amara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper describes the design and implementation of a system that identify the writer using off-line Arabic handwriting. Our approach is based on the combination of global and structural features. We used genetic algorithm for feature subset selection in order to eliminate the redundant and irrelevant ones. A modular Multilayer Perceptron (MLP) classifier was used. Experiments have shown writer identification accuracies reach acceptable performance levels with an average rate of 94.73% using optimal feature subset. Experiments are carried on a database of 180 text samples, whose text was made to ensure the involvement of the various internal shapes and letters locations within a word.

Keywords

Genetic Algorithm Feature Subset Optimal Subset Feature Subset Selection Optimal Feature Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sami Gazzah
    • 1
  • Najoua Essoukri Ben Amara
    • 2
  1. 1.Laboratoire des Systèmes et du Traitement de Signal (LSTS)Ecole National d’Ingénieurs de Tunis (ENIT) 
  2. 2.Ecole Nationale d’ingénieurs de Sousse 4000SousseTunisie

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