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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 697658, 8 pages
Research Article

Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection

Department of Electronics Convergence Engineering, Wonkwang University, 344-2 Shinyong Dong, Iksan, Jeonbuk 570-749, Republic of Korea

Received 6 March 2014; Accepted 31 July 2014; Published 18 August 2014

Academic Editor: Young-Sik Jeong

Copyright © 2014 Moon Kyou Song and Md. Mostafa Kamal Sarker. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, 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. This method is of a faster speed and higher accuracy than most of the existing methods used in LPD. Experimental results demonstrate that the average LPD rate is 98.38% and the computational time is approximately 49 ms.