Rapid license plate detection using Modest AdaBoost and template matching

License plate detection and recognition are vital yet challenging tasks for law enforcement agencies. This paper presents a license plate detection prototype system for a Macao law enforcement department using Modest Adaboost combined with template matching technique. Firstly, a machine learning algorithm, based on Modest AdaBoost which mostly aims for better generalization capability and resistance to overfitting, was applied to find out candidate license plates over the input images. In the second stage, template matching technique was employed to verify the license plate appearances in order to reduce false positives. This paper shows that the AdaBoost algorithm, which was originally used for face detection, has successfully been applied to solve the problems of license plate detection. Experimental results demonstrate high accuracy and efficiency of the proposed method.