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Automated Parking Management System using Image Processing Techniques

Vaidehi P. De, D. Ragavesh. Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Vaidehi P. De, D. Ragavesh
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  1. Vaidehi P De and D Ragavesh. Automated Parking Management System using Image Processing Techniques. International Journal of Applied Information Systems 11(3):6-10, August 2016. URL, DOI BibTeX

    	author = "Vaidehi P. De and D. Ragavesh",
    	title = "Automated Parking Management System using Image Processing Techniques",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August 2016",
    	volume = 11,
    	number = 3,
    	month = "Aug",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "6-10",
    	numpages = 5,
    	url = "",
    	doi = "10.5120/ijais2016451584",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Computerized systems being an integral part of the current era, an automated parking system is one of its most commonly used applications. This automated parking management system is designed to give access, exclusively to vehicles registered in the database. It takes the image of the vehicle and locates the license plate and identifies its license number. It then checks with the database to determine its access. This report is a detailed description of the image processing techniques used for Automatic Number Plate Recognition. It deals with computer vision and the various techniques used in image processing. The entire setup is compact enough to improve its portability and efficiency, whilst providing better security. The algorithms, train the machine for better pattern recognition to locate number plates. This system requires the vehicle to be physically present, so that security is not hampered with and access is denied to any vehicle that is not registered. The results were recorded to observe the quality of recognition.


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Artificial Neural Networking; Image Processing; Beaglebone Black; Edge Detection; Segmentation