2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-7.8
Paper Title MOBILE REGISTRATION NUMBER PLATE RECOGNITION USING ARTIFICIAL INTELLIGENCE
Authors Syed Talha Abid Ali, Abdul Hakeem Usama, Pakistan Institute of Engineering and Technology, Pakistan; Ishtiaq Rasool Khan, Muhammad Murtaza Khan, University of Jeddah, Saudi Arabia; Asif Siddiq, Pakistan Institute of Engineering and Technology, Pakistan
SessionMLR-APPL-IP-7: Machine learning for image processing 7
LocationArea E
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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).