A Novel Morphological Method for Detection and Recognition of Vehicle License Plates
S.H. Mohades Kasaei, S.M. Mohades Kasaei and S. A. Monadjemi
DOI : 10.3844/ajassp.2009.2066.2070
American Journal of Applied Sciences
Volume 6, Issue 12
Problem statement: License plate detection and recognition is an image-processing technique used to identify a vehicle by its license plate. This notable technology has got multiple applications in various traffic and security cases. To name but a few, toll roads, border control, security and car tracking are same of its applications. The main stage is the isolation of the license plate, from the digital image of the car obtained by a digital camera under different circumstances such as illumination, slop, distance and angle. Approach: This study presented a novel method of identifying and recognizing license plates based on the morphology and template matching. The algorithm started with preprocessing and signal conditioning. Next license plate is localized using morphological operators. Then a template matching scheme will be used to recognize the digits and characters within the plate. Results: The system was tested on Iranian car plate images and the performance was 97.3% of correct plates identification and localization and 92% of correct recognized characters. The results regarding the complexity of the problem and diversity of the test cases showed the high accuracy and robustness of the proposed method. The method could also be applicable for other applications in the transport information systems, where automatic recognition of registration plates, shields, signs and so on is often necessary. Conclusion: This system was customized for the identification of Iranian license plates. The results showed that this algorithm performs well on different types of vehicles including Iranian car and motorcycle plates as well as diverse circumstances. We believe that this system can be redesigned and tested for multi national car license plates in the future time regarding their own attributes.
© 2009 S.H. Mohades Kasaei, S.M. Mohades Kasaei and S. A. Monadjemi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.