Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching
TLDR
This paper attempts to review the various techniques and their usage of the Automatic Number Plate Recognition System (ANPR) in India and finds its accuracy was found to be 80.8% for Indian number plates.Abstract:
growing affluence of urban India has made the ownership of vehicles a necessity. This has resulted in an unexpected civic problem - that of traffic control and vehicle identification. Parking areas have become overstressed due to the growing numbers of vehicles on the roads today. The Automatic Number Plate Recognition System (ANPR) plays an important role in addressing these issues as its application ranges from parking admission to monitoring urban traffic and to tracking automobile thefts. There are numerous ANPR systems available today which are based on different methodologies. In this paper, we attempt to review the various techniques and their usage. The ANPR system has been implemented using template Matching and its accuracy was found to be 80.8% for Indian number plates.read more
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References
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Journal ArticleDOI
Automatic license plate recognition
TL;DR: The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module, the former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in alicense plate.
Journal ArticleDOI
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications
Christos-Nikolaos Anagnostopoulos,Ioannis Anagnostopoulos,Vassili Loumos,Eleftherios Kayafas +3 more
TL;DR: A review in the related literature presented in this paper reveals that better performance has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low.
Journal ArticleDOI
An Algorithm for License Plate Recognition Applied to Intelligent Transportation System
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A configurable method for multi-style license plate recognition
TL;DR: A new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format, which will result in a faster and more precise recognition.
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