Feature set optimization for the recognition of Arabic characters using genetic algorithms

Steven G. Schlosser; John M. Trenkle; Robert C. Vogt III

doi:10.1117/12.142800

13 April 1993 Feature set optimization for the recognition of Arabic characters using genetic algorithms

Steven G. Schlosser, John M. Trenkle, Robert C. Vogt III

Proceedings Volume 1838, 21st AIPR Workshop on Interdisciplinary Computer Vision: An Exploration of Diverse Applications; (1993) https://doi.org/10.1117/12.142800
Event: Applied Imaging Pattern Recognition, 1992, Washington, DC, United States

Abstract

This paper describes an investigation into the use of genetic algorithm techniques for selecting optimal feature sets in order to discriminate large sets of Arabic characters. Human experts defined a set of over 900 features from many different classes which could be used to help discriminate different characters from the Arabic character set. Each of the features was assigned a cost, based on the average amount of CPU time necessary to compute it for a typical character. The goal of the optimization was to find the subset of features which produced the best trade-off between recognition accuracy and computational cost. Using all of the features, or particular subsets, we obtained high recognition rates on machine-printed Arabic characters. Application of the genetic algorithm to selected subsets of characters and features demonstrates the ability of the method to significantly reduce the computational cost of the classification system and maintain or increase the recognition rate obtained with the complete set of features.

Citation Download Citation

Steven G. Schlosser, John M. Trenkle, and Robert C. Vogt III "Feature set optimization for the recognition of Arabic characters using genetic algorithms", Proc. SPIE 1838, 21st AIPR Workshop on Interdisciplinary Computer Vision: An Exploration of Diverse Applications, (13 April 1993); https://doi.org/10.1117/12.142800

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