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Learning-Based License Plate Detection Using Global and Local Features

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4 Author(s)
Huaifeng Zhang ; Comput. Vision Res. Group, Univ. of Technol., Sydney, NSW ; Wenjing Jia ; Xiangjian He ; Qiang Wu

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

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Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:2 )

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