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2006
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Learning-Based License Plate Detection Using Global and Local Features

12 years 6 months ago
Learning-Based License Plate Detection Using Global and Local Features
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.
Huaifeng Zhang, Qiang Wu, Wenjing Jia, Xiangjian H
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Huaifeng Zhang, Qiang Wu, Wenjing Jia, Xiangjian He
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