Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 697658, 8 pages.

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

Moon Kyou Song and Md. Mostafa Kamal Sarker

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Abstract

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.

Article information

Source
J. Appl. Math. Volume 2014, Special Issue (2014), Article ID 697658, 8 pages.

Dates
First available in Project Euclid: 1 October 2014

Permanent link to this document
http://projecteuclid.org/euclid.jam/1412176973

Digital Object Identifier
doi:10.1155/2014/697658

Citation

Song, Moon Kyou; Sarker, Md. Mostafa Kamal. Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection. J. Appl. Math. 2014, Special Issue (2014), Article ID 697658, 8 pages. doi:10.1155/2014/697658. http://projecteuclid.org/euclid.jam/1412176973.


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