Two-Stage License Plate Detection Using Gentle Adaboost and SIFT-SVM

Authors:
Wing Teng Ho;Hao Wooi Lim;Yong Haur Tay
Affiliations:
-;-;-
Venue:
ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
Year:
2009

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Abstract

This paper presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one alpha-numerical character, i.e. A..Z, 0..9) serve as the first stage classifier. In the second stage, a support vector machine (SVM) trained on scale-invariant feature transform (SIFT) descriptors obtained from training sub-windows were employed. A recall rate of 0.920792 and precision rate of 0.90185 was obtained.