Method for license plate detection based on improved AdaBoost algorithm
一种改进AdaBoost算法的车牌检测方法

Focusing on the disadvantages of classical AdaBoost algorithm, this paper mainly analyzed the issue that the training time for classifiers was time-consuming and in training process the sample weights were easily distorted and a new method was advanced to avoid the problems. The new method was to buffer the computational results of sorted feature values and regulate the updated rules of sample weights appropriately. As a result, using the method to train a cascade license plate, the experimental results show that the new method does not lead to the issue of weight distortion and time consuming like classical AdaBoost often does, and moreover, the training time is shortened to 50 percent with a high detection rate.