NDLI: Efficient method for vehicle license plate identification based on learning a morphological feature

Content Provider	IET Digital Library
Author	Wafy, Maged Madbouly, Ahmed M. M.
Abstract	The detection and identification of license plates from captured images have been widely studied over the past two decades. The demand for this technology in security and commercial applications ranges from traffic control organisation to parking management and vehicle tracking. License plate recognition can be divided into two steps: detection and identification. Recent algorithms have focused on the detection step, with few researchers studying identification. In this study, an algorithm is developed to detect and identify license plates. The algorithm is based on the fact that license plates have a semi-symmetric distribution of corner points; it utilises morphological feature learning. The algorithm runs in real time, is highly robust, and can identify whether the candidate region contains a license plate.
Starting Page	389
Ending Page	395
Page Count	7
ISSN	1751956X
Volume Number	10
e-ISSN	17519578
Issue Number	Issue 6, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/10/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2015.0064
Journal	IET Intelligent Transport Systems
Publisher Date	2016-08-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Captured Image Commercial Application Computer Vision And Image Processing Technique Feature Extraction Image Capture Image Recognition License Plate Detection License Plate Recognition Mathematical Morphology Morphological Feature Learning Object Detection Object Recognition Parking Management Security Application Semisymmetric Corner Point Distribution Traffic Control Traffic Control Organisation Traffic Engineering Computing Vehicle License Plate Identification Vehicle Tracking
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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