Conference Paper

A Novel License Plate Location Method Based on Neural Network and Saturation Information.

DOI: 10.1007/11589990_136 Conference: AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005, Proceedings
Source: DBLP

ABSTRACT In this paper, a novel license plate location algorithm for color image is presented. Firstly the neural networks are used
as filters for analyzing within small windows for an image and deciding whether each window contains a license plate or not
coarsely. And then we use the information which the license plate’s saturation value is different from the background’s, so
it can be used to locate license plate finely. At last, color pairs method is presented to prove whether the region we found
is the license plate region or not. The experimental results show that proposed algorithms are robust in dealing with the
license plate location in complex background.

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    ABSTRACT: A novel license plate locating approach based on the color and texture features is presented. Firstly, the input image is converted to the hue-saturation-intensity (HSI) color space. Then a target image is obtained by applying a sequence of image processing techniques to the hue and saturation component images. After that, the space-pixel histogram of the target image is analyzed and mathematically modeled, so that the horizontal candidate is extracted. Finally, discrete wavelet transform is performed on the candidate, and the sum of the first order difference of the DWT subimages highlights the texture information of the LP area, telling the precise position of the license plate. The proposed algorithm focuses on combining the color features with the texture features, improving the locating reliability. Experiment was conducted on a database of 332 images taken from various illumination situations. The license plate detecting rate of success is as high as 96.4%.
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