Multilanguage Number Plate Detection using Convolutional Neural Networks

Multilanguage Number Plate Detection using Convolutional Neural Networks

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-8
Year of Publication : 2020
Authors : Jatin Gupta, Vandana Saini, Kamaldeep Garg
DOI :  10.14445/22315381/IJETT-V68I8P204S

Citation 

MLA Style: Jatin Gupta, Vandana Saini, Kamaldeep Garg "Multilanguage Number Plate Detection using Convolutional Neural Networks" International Journal of Engineering Trends and Technology 68.8(2020):18-23. 

APA Style:Jatin Gupta, Vandana Saini, Kamaldeep Garg. Multilanguage Number Plate Detection using Convolutional Neural Networks  International Journal of Engineering Trends and Technology, 68(8),18-23.

Abstract
Object Detection is a popular field of research for recent technologies. In recent years, profound learning performance attracts the researchers to use it in many applications. Number plate (NP) detection and classification is analyzed over decades however, it needs approaches which are more precise and state, language and design independent since cars are now moving from state to another easily. In this paperwe suggest a new strategy to detect NP and comprehend the nation, language and layout of NPs. YOLOv2 sensor with ResNet attribute extractor heart is proposed for NP detection and a brand new convolutional neural network architecture is suggested to classify NPs. The detector achieves average precision of 99.57% and country, language and layout classification precision of 99.33%. The results outperforms the majority of the previous works and can move the area forward toward international NP detection and recognition.

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Keywords
component; Number Plate Detection; Number Plate Classification; NPD; Yolo Detector; CNN; Deep Learning.