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.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Huaifeng Zhang, Qiang Wu, Wenjing Jia, Xiangjian He
• Keywords: Computer Vision, Global Features, Global Statistical Features, ICPR 2006, Local Haar-like Features