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

Moon Kyou Song, Md Mostafa Kamal Sarker

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
1 Downloads (Pure)


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.
Original languageEnglish
Article number697658
Number of pages8
JournalIMA Journal of Applied Mathematics
Publication statusPublished - 18 Aug 2014
Externally publishedYes


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