An Error Back-Propagation Artificial Neural Networks Application in Automatic Car License Plate Recognition

  • Demetrios Michalopoulos
  • Chih-Kang Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2358)


License plate recognition involves three basics steps: 1) image preprocessing including thresholding, binarization, skew detection, noise filtering, and frame boundary detection, 2) character and number segmentations from the heading of the state area and the body of a license plate, 3) training and recognition on an Error Back-propagation Artificial Neural Networks (ANN). This report emphasizes on the implementation of modeling the recognition process. In particular, it deploys classical approaches and techniques for recognizing license plate numbers. The problems of recognizing characters and numbers from a license plate are described in details by examples. Also, the character segmentation algorithm is developed. This algorithm is then incorporated into the license plate recognition system.


Artificial Neural Network Image File License Plate Projection Profile Layout Analysis 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    John Miano, 1999. Compressed Image File Formats. Reading, Mass.: Addison Wesley Publishing Co.Google Scholar
  2. 2.
    L. O’Gorman „Image and Document Processing Techniques for the RightPage Electronic Library System,“ Proc. Int’l Conf. Pattern Recognition (ICRP), IEEE CS Press, Los Alamitos, Calif., 1992, pp. 260–263.Google Scholar
  3. 3.
    Jacek Zurada, Introduction to Artificial Neural Systems, West Publishing Company, 1992Google Scholar
  4. 4.
    W. Postl, „Detection of Linear Oblique Structures and Skew Scan in Digitized Document,“Proc. Int’l Conf. Document Analysis and Recognition (ICPR), IEEE CS Press, Los Alamitos, Calif, 1993, pp. 478–483.Google Scholar
  5. 5.
    R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, Wiley-Interscience, New York, 1973, pp. 330–336.zbMATHGoogle Scholar
  6. 6.
    W-Y. Wu and M-J. J. Wang. „Detecting the Dominant Points by the Curvature-Based Polygonal Approximation, “ CVGIP: Graphical Models and Image Processing, Vol. 55, No. 2, Mar. 1993, pp. 69–88.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Demetrios Michalopoulos
    • 1
  • Chih-Kang Hu
    • 1
  1. 1.Department of Computer ScienceCalifornia State UniversityFullerton

Personalised recommendations