License plate recognition system

Nadeem A. M. Khan; Ron J. De la Haye; Hans A. Hegt

doi:10.1117/12.323154

1 October 1998 License plate recognition system

Nadeem A. M. Khan, Ron J. De la Haye, Hans A. Hegt

Proceedings Volume 3460, Applications of Digital Image Processing XXI; (1998) https://doi.org/10.1117/12.323154
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

A powerful automated license plate recognition system is presented, which is able to read license numbers of cars, even under non-ideal circumstances. At the front-end of the system, there is a high-speed shutter camera and a frame grabber that delivers the digitized images of cars passing by. In a license plate segmentation step, the approximate positions of the four corner points of the plates are indicated. Due to the perspective view, these corner points may not correspond to a rectangle. By means of resampling, a rectangular license plate with a fixed size of 180 X 40 pixels is reconstructed. After image enhancement steps, the characters are approximately segmented, based on the properties of a vertical projection of the license plate. Next, the separate characters are normalized with respect to contrast, intensity and size. Each character image is projected on a low-dimensional space using the Karhunen- Loeve (KL) transform, containing the relevant information to distinguish it from other characters. A problem with this transformation arises, when the character is not properly segmented. We solved that problem by comparing the inverse KL transformed result with the original character. In case they differ significantly, this may indicate a major segmentation error, for which we can correct. This leads to a much improved segmentation and thus a transformation that holds the needed information for the classification. The KL transformed characters can be classified by several methods. We obtained good results by classifying the transformed characters with the help of the Euclidean distance. A misclassification rate of 0.4% was achieved with a rejection rate of 13%. Further development of the system, for which a number of recommendations are given, is expected to increase the system performance.

Citation Download Citation

Nadeem A. M. Khan, Ron J. De la Haye, and Hans A. Hegt "License plate recognition system", Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); https://doi.org/10.1117/12.323154

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