UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 3
March-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2103207


Registration ID:
306840

Page Number

1627-1636

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Title

Detection of Vehicular Number Plate System using Deep Learning Approach

Abstract

An automated system for recognizing vehicles’ license plates is a growing need in order to improve security and for traffic control applications, particularly in major urban areas. Automatic Number Plate Recognition (ANPR) is a type of an Intelligent Transport System. While numerous studies on plate identification, character segmentation and character recognition have been performed, several challenges still remain. An efficient Vehicle Detection System is necessary to ensure traffic monitoring. In the last 4-5 years, several image processing and learning methods have been developed such as Optical Character Recognition (OCR) technique. The aspect of object detection, though, hasn’t been exploited for ANPR framework in the previous researches focused on object detection. This research uses deep learning to leverage these object detection algorithms. You-Only-Look-Once (YOLO) and Convolutional Neural Network (CNN) have shown themselves to be the most efficient methods with regard to both supervised and unsupervised learning. This work studies several algorithms for object detection and deep learning, and compares their performance. The custom dataset was used to identify and recognize the license plate to ensure successful traffic management. We studied Deep Learning algorithms based on Image Segmentation using object detection algorithm through YOLO object detection technique in darkflow frameworks and character recognition based on CNN and also trained a model as base learner. This study establishes a method for real-time detection and identification of license plate, and drawing useful conclusions. The results of the simulation show that the deep learning methodology is more effective when detecting vehicle plates.

Key Words

Automatic Number Plate Recognition, Convolution Neural Network, Image Processing, Optical Character Recognition, License Plate, Deep Learning

Cite This Article

"Detection of Vehicular Number Plate System using Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.1627-1636, March-2021, Available :http://www.jetir.org/papers/JETIR2103207.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Detection of Vehicular Number Plate System using Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp1627-1636, March-2021, Available at : http://www.jetir.org/papers/JETIR2103207.pdf

Publication Details

Published Paper ID: JETIR2103207
Registration ID: 306840
Published In: Volume 8 | Issue 3 | Year March-2021
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.26210
Page No: 1627-1636
Country: Rudrapur, Uttarakhand, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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