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Proceedings Paper

Feature set optimization for the recognition of Arabic characters using genetic algorithms
Author(s): Steven G. Schlosser; John M. Trenkle; Robert C. Vogt
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Paper 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.

Paper Details

Date Published: 13 April 1993
PDF: 12 pages
Proc. SPIE 1838, 21st AIPR Workshop on Interdisciplinary Computer Vision: An Exploration of Diverse Applications, (13 April 1993); doi: 10.1117/12.142800
Show Author Affiliations
Steven G. Schlosser, Environmental Research Institute of Michigan (United States)
John M. Trenkle, Environmental Research Institute of Michigan (United States)
Robert C. Vogt, Environmental Research Institute of Michigan (United States)


Published in SPIE Proceedings Vol. 1838:
21st AIPR Workshop on Interdisciplinary Computer Vision: An Exploration of Diverse Applications
Jane Harmon, Editor(s)

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