An algorithm for multiscale license plate detection and rule-based character segmentation

Karalı, Ali Onur
License plate recognition (LPR) technology has great importance for the development of Intelligent Transportation Systems by automatically identifying the vehicles using image processing and pattern recognition techniques. Conventional LPR systems consist of license plate detection (LPD), character segmentation (CS) and character recognition (CR) steps. Successful detection of license plate and character locations have vital role for proper LPR. Most LPD and CS techniques in the literature assume fixed distance and orientation from the vehicle to the imaging system. Hence, application areas of LPR systems using these techniques are limited to stationary platforms. However, installation of LPR systems on mobile platforms is required in many applications and algorithms that are invariant to distance, orientation, and illumination should be developed for this purpose. In this thesis work, a LPD algorithm that is based on multi-scale vertical edge density feature, and a character segmentation algorithm based on local thresholding and connected component analysis operations are proposed. Performance of the proposed algorithm is measured using ground truth positions of the license plate and characters. Algorithm parameters are optimized using recall and precision curves. Proposed techniques for each step give satisfying results for different license plate datasets and algorithm complexity is proper for real-time implementation if optimized.


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Citation Formats
A. O. Karalı, “An algorithm for multiscale license plate detection and rule-based character segmentation,” M.S. - Master of Science, Middle East Technical University, 2011.