Bioscience Biotechnology Research Communications

An Open Access International Journal

P-ISSN: 0974-6455 E-ISSN: 2321-4007

Bioscience Biotechnology Research Communications

An Open Access International Journal

Rajesh Sharma R1, Sekar R2, T. Kar3, S. Chandra
Sekaran 4, U. Sakthi5 and T. Ch. Anil Kumar6

1Department of CSE, SoEEC, Adama Science and Technology University, Shewa, Ethiopia.

2Department of Electronics and Communication, Koneru Lakshmaiah Education Foundation,

Vaddeswaram, Guntur (DT), Andhra Pradesh, India.

3School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India.

4Department of CSE, P.S.V College of Engineering & Technology, Krishnagiri, Tamil Nadu, India.

5Department of CSE, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, India.

6Department of Mechanical Engineering, Vignan’s Foundation for Science Technology

and Research, Vadlamudi, Guntur, Andhra Pradesh, India.

Corresponding author email: sharmaphd10@gmail.com

Article Publishing History

Received: 13/05/2021

Accepted After Revision: 17/07/2021

ABSTRACT:

License Plate Recognition (LPR) is an advent research in Intelligent Transportation System (ITS) since it poses serious challenges in relation with image recognition. The robustness of ALPR gets reduced in real-world complex due to low quality images occurs from poor illumination, weather condition, complex background, perspective distortions and night light. In this paper, a deep neural network is used for classification and recognition of number plates in real-time moving vehicle under complex scenarios that mitigates the challenges associated. We develop a classification model using a deep learning algorithm namely Generative Adversarial Network(GAN) for the detection of number plates. The GAN network structure is optimized with 20-classes of CNN for the detection of number plates with higher accuracy. Such GAN forms an efficient network that performs classification with reduced errors. The simulation is conducted using python simulator to obtain the classification rates of the model. The extensive simulation shows that the GAN model obtains improved robustness against the real-time vehicle detection and improved scalability in terms of high classification accuracy.

KEYWORDS:

Number Plates,Deep Learning, Artificial Intelligence, Real-Time Classification

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