Learning-based license plate detection using global and local features

Publication Type:
Conference Proceeding
Citation:
Proceedings - International Conference on Pattern Recognition, 2006, 2 pp. 1102 - 1105
Issue Date:
2006-12-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2006005649.pdf302.33 kB
Adobe PDF
This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost learning algorithm is used to build up the other classifiers based on selected local Haar-like features. Combining the classifiers using the global features and the local features, we obtain a cascade classifier. The classifiers based on global features decrease the complexity of the system. They are followed by the classifiers based on local Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The encouraging detection rate is achieved in the experiments. © 2006 IEEE.
Please use this identifier to cite or link to this item: