Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1952
Title: GAN-based synthetic data augmentation for increased CNN performance in Vehicle Number Plate Recognition
Authors: Kukreja V
Kumar D
Kaur A
Geetanjali
Sakshi
Keywords: Convolutional Neural Network
Data Augmentation
Image processing
Stochastic gradient descent
Cross-Entropy
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In today's modern era, parking remains a big problem for a lot of people. This problem consumes a person individual's time by finding the right spot for parking. In the current research, the concept of an automatic parking system using the vehicle license plate or the number plate recognition is discussed. It will improve the process with much less hassle by removing human interaction. It will also lead to advancement in the security of vehicles evading the requirement of a slip or a magnetic card which is used to goes in and out for registering vehicles in a parking place. The researcher uses image processing algorithms to make an entry in the database of the parking automatically. AVNPR (Automatic Vehicle Number Plate Recognition) is used for the identification of the number of plates. Due to noise issues, deep algorithms like CNN (convolutional neural networks), RNN (recurrent neural networks) do not correctly recognize the miss-identification of the numbers in the vehicle plate. This problem is rectified by the authors by using the GAN (Generative adversarial networks) algorithm. GAN helps to create high-resolution images from a single low-resolution image. After applying the GAN, the classification of the vehicle plate is done through CNN. During experimentation, the proposed approach achieves 99.39% recognition accuracy for a vehicle number plate. Hence, the proposed system is suitable for identifying the numbers in the vehicle number plate automatically. Moreover proposed system compared with existing models, it has been found that it has achieved higher accuracy than the other models.
URI: 10.1109/ICECA49313.2020.9297625
http://hdl.handle.net/123456789/1952
Appears in Collections:Conferences

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