Multilanguage Number Plate Detection using Convolutional Neural Networks
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Jatin Gupta, Vandana Saini, Kamaldeep Garg
|DOI : 10.14445/22315381/IJETT-V68I8P204S|
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.
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.
 You-Shyang Chen and Ching-Hsue Cheng, "A Delphibased rough sets fusion model for extracting payment rules of vehicle license tax in the government sector," Expert Systems with Applications, vol. 37, no. 3, pp. 2161- 2174, 2010.
 Anton Satria Prabuwono and Ariff Idris, "A Study of Car Park Control System Using Optical Character Recognition ," in International Conference on Computer and Electrical Engineering, 2008, pp. 866-870
 A Albiol, L Sanchis, and J.M Mossi, "Detection of Parked Vehicles Using Spatiotemporal Maps," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1277- 1291, 2011.
 Christos Nikolaos E. Anagnostopoulos, Ioannis E. Anagnostopoulos, Ioannis D. Psoroulas, Vassili Loumos, and Eleftherios Kayafas, License Plate Recognition From Still Images and Video Sequences: A Survey, vol. 9, no. 3, pp. 377- 391, 2008
 Christos Nikolaos E. Anagnostopoulos, Ioannis E. Anagnostopoulos, Vassili Loumos, and Eleftherios Kayafas, "A License Plate-Recognition Algorithm for Intelligent Transportation System Applications," pp. 377- 392, 2006
 H. Erdinc Kocer and K. Kursat Cevik, "Artificial neural netwokrs based vehicle license plate recognition," Procedia Computer Science, vol. 3, pp. 1033-1037, 2011.
 A Roy and D.P Ghoshal, "Number Plate Recognition for use in different countries using an improved segmenation," in 2nd National Conference on Emerging Trends and Applications in Computer Science(NCETACS), 2011, pp. 1-5.
 R. Girshick, J. Donahue, T. Darrell, J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, 2014.
 Zhigang Zhang and Cong Wang, "The Reseach of Vehicle Plate Recogniton Technical Based on BP Neural Network," AASRI Procedia, vol. 1, pp. 74-81, 2012
 K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556, 2014.
 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition, 2015.
 K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition,” IEEE Conference on Computer Vision and Pattern Recognition, 2016.
 D. Virmani, P. Girdhar, P. Jain, P. Bamdev, “FDREnet: Face Detection and Recognition Pipeline ,” Engineering, Technology & Applied Science Research, Vol. 9, No. 2, pp. 3933-3938, 2019.
 L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X Liu, M. Pietikäinen, “Deep Learning for Generic Object Detection: A Survey, ” International Journal of Computer Vision, Vol. 128, No. 2, pp. 261-318, 2020.
 R. Girshick, “Fast R-CNN,” IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, 2015.
 S. Ren, K. He, R. Girshick, J. Sun, “Faster R-CNN: Towards Realtime Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1137-1149, 2017.
 Yifan Zhu, Han Huang, Zhenyu Xu, Yiyu He, and Shiqiu Liu, "Chinese-style Plate Recognition Based on Artificaial Neural Network and Statistics," Procedia Engineering, vol. 15, pp. 3556-3561, 2011..
 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition, 2016.
 W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A. C. Berg, “SSD: Single Shot Multibox Detector,” European Conference on Computer Vision, 2016, pp. 21-37.
 J. Redmon, A. Farhadi, “Yolo9000: Better, Faster, Stronger,” IEEE Conference on Computer Vision and Pattern Recognition, 2017.
 J. Redmon, A. Farhadi, “Yolov3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767, 2018.
 S. Du, M. Ibrahim, M. Shehata, W. Badawy, “Automatic Number Plate Recognition (ANPR): A State-of-the-Art Review,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 23, No. 2, pp. 311–325, Feb 2013.
 J. Han, J. Yao, J. Zhao, J. Tu, Y. Liu, “Multi-Oriented and Scale-Invariant Number Plate Detection Based on Convolutional Neural Networks,” Sensors, Vol. 19, No. 5, pp. 1175, 2019.
 L. Xie, T. Ahmad, L. Jin, Y. Liu, S. Zhang, “A New CNN-Based Method for Multi-Directional Car Number Plate Detection,” IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 2, pp. 507-517, 2018.
 S. G. Kim, H. G. Jeon, H. I. Koo, “Deep-Learning-Based Number Plate Detection Method Using Vehicle Region Extraction,” IEEE Electronics Letters, Vol. 53, No. 15, pp. 1034-1036, 2017.
 W. Min, X. Li, Q. Wang, Q. Zeng, Y. Liao, “New Approach to Vehicle Number Plate Location Based on New Model YOLO-L and Plate Pre-Identification,” IET Image Processing, Vol. 13, No. 7, pp. 1041-1049, 2019.
 Cynthia Lum, Julie Hibdon, Breanne Cave, Christopher S. Koper, and Linda Merola, "License plate reader(LRP) police patrols in crime hot spots: an experimental evaluation in two adjacent jurisdictionss," Journal of Experimel Criminology, Springer Netherlands, , pp. 321-345, 2011..
 Yushuang Tian, Kim-Hui Yap, and Yu He, "Vehicle license plate super-resolution using soft learning prior," Multimedia Tools and Applications, Springer US, pp. 519-535, 2019.
component; Number Plate Detection; Number Plate Classification; NPD; Yolo Detector; CNN; Deep Learning.