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License Plate Recognition

Authors: Navdeep Bohra; Rishabh Rishabh; Mallika Lather; Nikunj Chawla; Ashish Kumari;

License Plate Recognition

Abstract

Automatic recognition of license plate has turned very important in our day by day life as a result of the significant rise in the traffic and vehicles on road and the extensive transportation mechanism makes it impossible to be totally noticeable and monitor-able by humans. Examples are such a significant number of like traffic checking, following stolen automobiles, overseeing stopping toll, red-light infringement authorization, outskirt and tradition checkpoints. However, it's an exceptional testing issue, because of the decent variety of license plate groups, various scales, turns and nonuniform brightening conditions during picture procurement. This paper essentially presents an Automatically Recognizing License Plates (ANPR) which having a trained system for detecting a number plate if any, and then optically recognize the text, that is number written on the plate after pre-processing the image with certain thresholding techniques.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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