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MOBILE REGISTRATION NUMBER PLATE RECOGNITION USING ARTIFICIAL INTELLIGENCE

Citation Author(s):
Syed Talha Abid Ali, Abdul Hakeem Usama, Ishtiaq Rasool Khan, Muhammad Murtaza Khan, Asif Siddiq
Submitted by:
Syed Talha Ali
Last updated:
25 September 2021 - 5:33am
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Syed Talha Abid Ali
Paper Code:
2757

Abstract 

Abstract: 

Automatic License Plate Recognition (ALPR) for years has remained a persistent topic of research due to numerous practicable applications, especially in the Intelligent Transportation system (ITS). Many currently available solutions are still not robust in various real-world circumstances and often impose constraints like fixed backgrounds and constant distance and camera angles. This paper presents an efficient multi-language repudiate ALPR system based on machine learning. Convolutional Neural Network (CNN) is trained and fine-tuned for the recognition stage to become more dynamic, plaint to diversification of backgrounds. For license plate (LP) detection, a newly released YOLOv5 object detecting framework is used. Data augmentation techniques such as gray scale and rotation are also used to generate an augmented dataset for the training purpose. This proposed methodology achieved a recognition rate of 92.2%, producing better results than commercially available systems, PlateRecognizer (69%) and OpenALPR (77%). Our experiments validated that the proposed methodology can meet the pressing requirement of real-time analysis in Intelligent Transportation System (ITS).

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