License plate detection using adaptive morphological closing and local adaptive thresholding

Abstract

Automatic License Plate Recognition (ALPR) is base of many Intelligent Transformation Systems (ITS) services. Many ALPR systems have usually three steps, License Plate Detection (LPD), character segmentation and character recognition. LPD is the first and main step in ALPR. There are many algorithms for LPD, while detecting a license plate in different conditions is still a complex task. The goal of this paper is proposing an algorithm to extract license plate in different conditions. The proposed approach has three following steps, adaptive morphological closing, local adaptive thresholding and morphological opening. Experimental results using some real dataset show that the detection rate of the proposed approach is higher than some related works. In addition, the computational time of the proposed approach is less than other techniques.