29 November 2016 System recognizing Bahamian license plate with touching characters
Jingyu Dun, Sanyuan Zhang, Xiuzi Ye, Yin Zhang
Author Affiliations +
Abstract
Various methods are proposed for license plate recognition, but none of them are universal. Some common methods for license plate localization, character extraction, and recognition are analyzed. Then a system is proposed to recognize the Bahamian license plate with touching characters. A vertical edge-based method with a modified sliding window technique is used to locate the license plate, and a machine learning process is used to trim the region. The located license plate is rectified by using the minimum enclosing box and the stroke width value. Then the vertical projection and pairs of extreme points are combined to segment the characters. Finally, a deep learning method is used to recognize the characters. 2996 images are experimented on and the total recognition accuracy achieves 83.29%. Typical methods of each stage are implemented to compare with the proposed methods. In addition, the proposed system is experimented on a public dataset to show the generalization ability of the system. The experimental results show that the proposed system performs better than the other methods and is able to be used in a real-time application.
© 2016 SPIE and IS&T 1017-9909/2016/$25.00 © 2016 SPIE and IS&T
Jingyu Dun, Sanyuan Zhang, Xiuzi Ye, and Yin Zhang "System recognizing Bahamian license plate with touching characters," Journal of Electronic Imaging 25(6), 063009 (29 November 2016). https://doi.org/10.1117/1.JEI.25.6.063009
Published: 29 November 2016
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Image segmentation

Binary data

Image processing

Detection and tracking algorithms

Imaging systems

Optical character recognition

Principal component analysis

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