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Vehicle License Plate Recognition System Based on Deep Learning in Natural Scene

Ze Chen, Leiming Yan*, Siran Yin, Yuanmin Shi
School of Computer & Software, Nanjing University of Information Science & Technology, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, China
* Corresponding Author: Leiming Yan. Email:

Journal on Artificial Intelligence 2020, 2(4), 167-175.

Received 30 August 2020; Accepted 19 October 2020; Issue published 31 December 2020


With the popularity of intelligent transportation system, license plate recognition system has been widely used in the management of vehicles in and out of closed communities. But in the natural environment such as video monitoring, the performance and accuracy of recognition are not ideal. In this paper, the improved Alex net convolution neural network is used to remove the false license plate in a large range of suspected license plate areas, and then the projection transformation and Hough transformation are used to correct the inclined license plate, so as to build an efficient license plate recognition system in natural environment. The proposed system has the advantages of removing interference objects in a large area and accurately locating the license plate. The experimental results show that the localization success rate is 98%, and our system is feasible and efficient.


License plate location; deep learning; natural scene; convolution neural network

Cite This Article

Z. Chen, L. Yan, S. Yin and Y. Shi, "Vehicle license plate recognition system based on deep learning in natural scene," Journal on Artificial Intelligence, vol. 2, no.4, pp. 167–175, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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