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Exploring Elitism in Genetic Algorithms for License Plate Recognition with Michigan-Style Classifiers

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Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

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Abstract

This document describes the application of Genetic Algorithms (GAs) in the recognition of the printed characters in Colombian vehicular license plates. First of all, the accuracy achieved by a genetic algorithm with simple elitism is contrasted with the accuracy of a population elitism-based genetic algorithm. Due to the notorious difficulty of using the standard technique of dedicating from, 70 to 80% of the available data to train the classifier, and the rest of data for its validation, here, two methods to generate the training data are described, as well as some other techniques to improve the classifier performance.

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Author information

Authors and Affiliations

  1. Corporación Unificada Nacional de Educación Superior, Bogotá, Colombia

    Dante Giovanni Sterpin Buitrago

  2. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Fernando Martínez Santa

Authors
  1. Dante Giovanni Sterpin Buitrago
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  2. Fernando Martínez Santa
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Corresponding author

Correspondence to Dante Giovanni Sterpin Buitrago .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Buitrago, D.G.S., Santa, F.M. (2017). Exploring Elitism in Genetic Algorithms for License Plate Recognition with Michigan-Style Classifiers. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_46

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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