Paper Title
Automatic Number Plate Recognition System Using Deep Learning
Article Identifiers
: Keywords
Number Plate detection, Number plate recognition system, Character segmentation, Character recognition, , CNN, Deep Learning, Yolo algorithm.
Abstract
Automatic number plate recognition (ANPR) is one of the intelligent transportation methods that provides a safe medium of transportation and monitoring planning. It is used by many two-wheeler vehicles as well as four-wheeler vehicles to enhance traffic controlling, routing, parking system, toll compendium, governance, and insuring highway law. ANPR is one of the smart transportation methods that provides a safe medium of transportation and monitoring planning. In order for us to be able to identify and recognize number plates, numerous methods and algorithms have been devised. This study demonstrates several different approaches to deep learning that can be used to recognise and identify number plates. This system is composed of three istinct components: the detection of licence plates, the segmentation of the image, and the recognition of characters. The Yolo approach is utilised in the system that we have developed for the detection of licence plates. After that, various filters are applied, and finally, the characters are segmented. After all of the characters have been segmented, the Convolutional Neural Network is utilised to recognize them.
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How To Cite (APA)
Bhushan Agashe, Girish Zarbade, Gaurav Chopra , Saurabh Borghare , & Supriya Sawwashere (April-2023). Automatic Number Plate Recognition System Using Deep Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), d432-d437. https://ijnrd.org/papers/IJNRD2304361.pdf
Issue
Volume 8 Issue 4, April-2023
Pages : d432-d437
Other Publication Details
Paper Reg. ID: IJNRD_190987
Published Paper Id: IJNRD2304361
Downloads: 000122012
Research Area: Engineering
Author Type: Indian Author
Country: Nagpur, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2304361.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304361
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