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License Plate Detection Based on Genetic Neural Networks, Morphology, and Active Contours

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 6098)


This paper describes a new method for License Plate Detection based on Genetic Neural Networks, Morphology, and Active Contours. Given an image is divided into several virtual regions sized 10×10 pixels, applying several performance algorithms within each virtual region, algorithms such as edge detection, histograms, and binary thresholding, etc. These results are used as inputs for a Genetic Neural Network, which provides the initial selection for the probable situation of the license plate. Further refinement is applied using active contours to fit the output tightly to the license plate. With a small and well–chosen subset of images, the system is able to deal with a large variety of images with real–world characteristics obtaining great precision in the detection. The effectiveness for the proposed method is very high (97%). This method will be the first stage of a surveillance system which takes into account not only the actual license plate but also the model of the car to determine if a car should be taken as a threat.


  • Active Contour
  • License Plate
  • Electronic Toll Collection
  • License Plate Recognition
  • Slope Plate

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© 2010 Springer-Verlag Berlin Heidelberg

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Olivares, J., Palomares, J.M., Soto, J.M., Gámez, J.C. (2010). License Plate Detection Based on Genetic Neural Networks, Morphology, and Active Contours. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13032-8

  • Online ISBN: 978-3-642-13033-5

  • eBook Packages: Computer ScienceComputer Science (R0)