Modeling and implementing two-stage AdaBoost for real-time vehicle license plate detection

License plate (lp) detection is themost imperative part of the automatic lp recognition system. in previous years,differentmethods,techniques,and algorithms have been developed for lp detection (lpd) systems.this paper proposes to automatical detection of car lps via image processing techniques based on classifier or machine learning algorithms. in this paper,we propose a real-time and robust method for lpd systems using the two-stage adaptive boosting (adaboost) algorithm combined with different image preprocessing techniques. haar-like features are used to compute and select features from lp images.the adaboost algorithm is used to classify parts of an image within a search window by a trained strong classifier as either lp or non-lp. adaptive thresholding is used for the image preprocessing method applied to those images that are of insufficient quality for lpd. thismethod is of a faster speed and higher accuracy thanmost of the existing methods used in lpd. experimental results demonstrate that the average lpd rate is 98.38% and the computational time is approximately 49ms. copyright © 2014 m. k. song and md. m. k. sarker.