A PROPOSED METHOD OF LICENSE PLATE DETECTION AND RECOGNITION FOR PORTABLE DEVICES

  • Nguyen Tien Dung Hung Yen University of Technology and Education
Keywords: License Plate Recognition, Deep learning, Convolutional Neural Network

Abstract

In this paper, we describe a new method of License Plate Recognition where LP in different conditions due to the protability of the handset. In this situations, the LP might be considerably distorted due to oblique views. The proposed algorithms applied to locate the car license plate include YOLO platfom to indentify License Plate area, and modified location license plate algorithms for improvement car license plate detection result using Tesseract Optical Character Recognition (OCR) method to obtain the final result. In addition, we also used Jetson TX2 system with 256 NVDIA CUDA core and suitable for handheld decives. Our proposed approach obtained better results for removing noise and locating characters in the plate. The promising experimental results demonstrated that our proposed method is efficient and stable enough for problem identification car license plate.

References

Anagnostopoulos, C.N., Anagnostopoulos, I., Psoroulas, I., Loumos, V., Kayafas, E., “License Plate Recognition From Still Images and Video Sequences: A Survey”. IEEE Transactions on Intelligent Transportation Systems, sep 2008, 9(3), pp. 377–391.

Bulan, O., Kozitsky, V., Ramesh, P., Shreve, M., “Segmentation- and AnnotationFree License Plate Recognition With Deep Localization and Failure Identification”. IEEE Transactions on Intelligent Transportation Systems, sep 2017, 18(9), pp. 2351–2363.

Delmar Kurpiel, F., Minetto, R., Nassu, B.T., “Convolutional neural networks for license plate detection in images”. In: 2017 IEEE International Conference on Image Processing (ICIP), sep 2017, pp. 3395–3399, IEEE.

Du, S., Ibrahim, M., Shehata, M., Badawy, W., “Automatic License Plate Recognition (ALPR): A State-of-the-Art Review”. IEEE Transactions on Circuits and Systems for Video Technology, feb 2013, 23(2), pp. 311–325.

Gonalves, G.R., da Silva, S.P.G., Menotti, D., Schwartz, W.R., “Benchmark for license plate character segmentation”. Journal of Electronic Imaging, 2016, 25(5), pp. 1–5.

Gupta, A., Vedaldi, A., Zisserman, A., “Synthetic data for text localisation in natural images”. In: IEEE Conference on Computer Vision and Pattern Recognition, 2016.

H. Li, P. Wang, M. You, C. Shen, “Reading car license plates using deep neural networks”. Image Vis Comput, 2018.

G.S. Hsu, J.C. Chen, Y.Z. Chung, “Application-oriented license plate recognition”. IEEE Trans Veh Techno, 2013.

Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., Gon¸calves, G.R., Schwartz, W.R., Menotti, D., “A robust real-time automatic license plate recognition based on the YOLO detector”. CoRR abs/1802.09567, 2018.

Li, H., Wang, P., Shen, C.: “Towards end-to-end car license plates detection and recognition with deep neural networks”. CoRR abs/1709.08828, 2017.

Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q, “Densely Connected Convolutional Networks”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), jul 2017, pp. 2261–2269. IEEE.

Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., Gon¸calves, G.R., Schwartz, W.R., Menotti, D., “A robust real-time automatic license plate recognition based on the YOLO detector”. CoRR abs/1802.09567, 2018.

Zhou, X., Zhu, M., Leonardos, S., Daniilidis, K., “Sparse representation for 3d shape estimation: A convex relaxation approach”. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(8), pp. 1648–1661.

Published
2022-06-14
How to Cite
Nguyen Tien Dung. (2022). A PROPOSED METHOD OF LICENSE PLATE DETECTION AND RECOGNITION FOR PORTABLE DEVICES. UTEHY Journal of Science and Technology, 32, 58-63. Retrieved from https://tapchi.utehy.edu.vn/index.php/jst/article/view/500