Author(s)

Xueju Hao ¹

Affiliation(s)

¹ School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

Corresponding Author

Xueju Hao

ABSTRACT

With the rapid development of intelligent transportation systems, license plate recognition technology, as a core link of vehicle identity authentication, has important application value in traffic monitoring, parking lot management, violation law enforcement and other fields. This paper proposes a license plate character recognition scheme based on deep learning, which realizes accurate localization of license plate regions through edge detection, completes character recognition tasks using Convolutional Neural Networks (CNN), and compares and analyzes the performance differences between single-stage and two-stage recognition methods. Experimental results show that the two-stage recognition method has higher accuracy in complex environments, while the single-stage method has faster recognition speed. This research provides theoretical reference and technical support for the engineering application of license plate recognition systems. 

KEYWORDS

License Plate Recognition; Deep Learning; Convolutional Neural Network; Edge Detection; Character Recognition

CITE THIS PAPER

Xueju Hao, Research on License Plate Character Recognition Based on Deep Learning. Journal of Electronics and Information Science (2025) Vol. 10: 135-140. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100216.

REFERENCES

[1] Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arxiv preprint arxiv:1804.02767 (2018).
[2] Lecun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (2002): 2278-2324.
[3] Laroca, Rayson, et al. "A robust real-time automatic license plate recognition based on the YOLO detector." 2018 international joint conference on neural networks (ijcnn). IEEE, 2018.
[4] Li, Hui, Peng Wang, and Chunhua Shen. "Toward end-to-end car license plate detection and recognition with deep neural networks." IEEE Transactions on Intelligent Transportation Systems 20.3 (2018): 1126-1136.
[5] Sahu, Chinmaya Kumar, et al. "A comparative analysis of deep learning approach for automatic number plate recognition." 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, 2020.

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