An Efficient Method of License Plate Location

Abstract

This paper presents a novel approach of license plate location. The proposed algorithm involves the following three steps. First, the vertical edges of the vehicle image are extracted by Sobel operator. Second, HSV color space and integral image are employed to locate candidates in yellow license plates and non-yellow license plates. Finally, connected component analysis is to locate the region of license plate accurately. Experimental results on a large volume of natural-scene vehicle plate image sets, which are extracted from low-quality video sequences, demonstrate that our technique achieves a verification rate of around 95% on yellow license plates and 99% on non-yellow ones. The total time of processing one yellow image is less than 0.1s and the non-yellow one is less than 0.05s, meeting the requirements of real-time application.

Automated License Plate Recognition: A Survey on Methods and Techniques

“…When two horizontal edges are concerned it is known as horizontal edge detection and for two vertical edges, it is called vertical edge detection. Several studies have used Sobel filter for edge detection [6], [7], [17], [18]. The Sobel filter has two 3 × 3 convolutional matrices and one is dedicated for vertical and the other for horizontal edge detection.…”

“…One of the major drawbacks of this approach is its responsiveness to noise. In [7], [17], Luo et al and Sarfraz et al have used a Sobel filter to extract only the vertical edges. However, using only the horizontal lines leads to erroneous results as the vehicle bumper can be mistaken as a horizontal edge of the license plate.…”

“…Existing algorithms use traditional computer vision techniques and deep learning methods with object detection to locate the license plate in an image. Traditional computer vision techniques are mainly based on the features of the license plate such as shape [6], [7], [17], [18], colour [19]- [22], symmetry [23], texture [24]- [28], etc. In the second stage, the license plate is segmented and the characters are extracted using some common techniques such as mathematical morphology [29], connected components, relaxation labelling, and vertical and horizontal projection [30].…”

IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities

“…In [11], a color based method combined with the texture characteristics is proposed to try to detect license plate from the color image. In [12] and [13], the combination of edge information and plate color are utilized to identify the vehicle license plates.…”

Automatic Number Plate Recognition using Canny Edge Detector, Sliding Concentric Windows and Linear Discriminant Analysis

“…On the other hand, some approaches take advantage of color features. Lin Luo[4] creates a new and effective approach of license plate location. The following three key sections are involved in this proposed algorithm.Firstly, Sobel operator is used to extract the vertical edges of the vehicle image.…”