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A Real-Time Implementation of License Plate Recognition (Lpr) System (en Inglés)
Santosh Kumar Sahoo (Autor)
·
Grin Verlag
· Tapa Blanda
A Real-Time Implementation of License Plate Recognition (Lpr) System (en Inglés) - Santosh Kumar Sahoo
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Reseña del libro "A Real-Time Implementation of License Plate Recognition (Lpr) System (en Inglés)"
Master's Thesis from the year 2010 in the subject Engineering - Computer Engineering, grade: A+, Gandhi Institute of Engineering and Technology, language: English, abstract: With increasing number of population and higher rate of development the problem of road accident is also increasing rapidly. So the basic concept is to develop a model that can be useful as a security system in the society and can monitoring the vehicle speed. A License Plate Recognition (LPR) System is one kind of an Intelligent Transport monitoring System and is of considerable interest because of its potential applications in highway electronic toll collection and traffic monitoring systems. This type of applications puts high demands on the reliability of an LPR System. A lot of work has been done regarding LPR systems for Korean, Chinese, European and US license plates that generated many commercial products. However, little work has been done for Indian license plate recognition systems. The purpose of this thesis was to develop a real time application which recognizes license plates from cars at a gate, for example at the entrance of a parking area or a border crossing. The system, based on regular PC with video camera, catches video frames which include a visible car license plate and processes them. Once a license plate is detected, its digits are recognized, displayed on the User Interface or checked against a database. The focus is on the design of algorithms used for extracting the license plate from a single image, isolating the characters of the plate and identifying the individual characters. The proposed system has been implemented using Vision Assistant 7,1 and LabVIEW 7,1. The performance of the system has been investigated on real images of about 100 vehicles. The recognition of about 98% vehicles shows that the system is quite efficient.
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