Libyan Vehicle License Plate Recognition with Support Vector Machine


  • Aeyman M. Hassan School of Computer Engineering, University of Zawia, Zawia, Libya.
  • Sami A. Ghoul School of Computer Engineering, University of Zawia, Zawia, Libya.
  • Aya A. Alkabir School of Computer Engineering, University of Zawia, Zawia, Libya.



Support Vector Machine, Number Recognition, Arduino


Computer vision has become widely used in many aspects of our daily lives. There are a great number of applications that consider computer vision as a core part of them, such as those associated with law enforcement. This paper presents the design and implementation of a model for a vehicle building entrance, using a license plate recognition algorithm for Libyan vehicles. In addition, a small dataset of Libyan vehicles was created for research purposes. As with most recognition systems, there are mainly three stages to be distinguished: plate detection using vertical and horizontal histograms, character segmentation is performed through a connected-component labeling algorithm, and finally, optical character recognition (OCR) by using support vector machines (SVMs). An Arduino board was used to control the gate opening and closing processes according to the authorized vehicles stored in the database. Ultrasonic sensors were used to detect a vehicle stop at the gate. The system was programmed with MATLAB executed on a 2.20GHz Core i7 CPU, 8 GB RAM, Windows 10. Despite the limited size of the vehicle images dataset, the experiments showed promising performance in terms of average accuracy estimated at 83.3%, and the computation time was 5 seconds.


Download data is not yet available.


Abdella, A. A. E. S. (2016). Libyan licenses plate recognition using template matching method. Journal of Computer and Communications, 4(07), 62.

Abdullah, S. N. H. S., Omar, K., Sahran, S., & Khalid, M. (2009). License plate recognition based on support vector machine. 2009 International Conference on Electrical Engineering and Informatics,

Algablawi, W., BenAnaif, W., & Ganoun, A. (2013). Libyan Vehicle License Plate Recognition System. International Conference on Elecetrical and Computer Engineering,

Arth, C., Limberger, F., & Bischof, H. (2007). Real-time license plate recognition on an embedded DSP-platform. 2007 IEEE Conference on Computer Vision and Pattern Recognition,

Björklund, T., Fiandrotti, A., Annarumma, M., Francini, G., & Magli, E. (2019). Robust license plate recognition using neural networks trained on synthetic images. Pattern Recognition, 93, 134-146.

Chen, R.-C. (2019). Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning. Image and Vision Computing, 87, 47-56.

Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05),

Duan, T. D., Du, T. H., Phuoc, T. V., & Hoang, N. V. (2005). Building an automatic vehicle license plate recognition system. Proc. Int. Conf. Comput. Sci. RIVF,

He, L., Chao, Y., Suzuki, K., & Wu, K. (2009). Fast connected-component labeling. Pattern Recognition, 42(9), 1977-1987.

Henry, C., Ahn, S. Y., & Lee, S.-W. (2020). Multinational license plate recognition using generalized character sequence detection. IEEE Access, 8, 35185-35199.

Jabar, K. A., & Nasrudin, M. F. (2016). Libyan vehicle plate recognition using region-based features and probabilistic neural network. Journal of Theoretical and Applied Information Technology, 94(1), 104-114.

Jagannathan, J., Sherajdheen, A., Deepak, R. M. V., & Krishnan, N. (2013). License plate character segmentation using horizontal and vertical projection with dynamic thresholding. 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN),

Kanopoulos, N., Vasanthavada, N., & Baker, R. L. (1988). Design of an image edge detection filter using the Sobel operator. IEEE Journal of solid-state circuits, 23(2), 358-367.

Kasaei, S. H., Kasaei, S. M., & Kasaei, S. A. (2010). New Morphology-Based Method for RobustIranian Car Plate Detection and Recognition. International Journal of Computer Theory and Engineering, 2(2), 264.

Laroca, R., Severo, E., Zanlorensi, L. A., Oliveira, L. S., Gonçalves, G. R., Schwartz, W. R., & Menotti, D. (2018). A robust real-time automatic license plate recognition based on the YOLO detector. 2018 international joint conference on neural networks (ijcnn),

Li, H., & Shen, C. (2016). Reading car license plates using deep convolutional neural networks and LSTMs. arXiv preprint arXiv:1601.05610.

Noble, W. S. (2006). What is a support vector machine? Nature biotechnology, 24(12), 1565-1567.

Selmi, Z., Halima, M. B., Pal, U., & Alimi, M. A. (2020). DELP-DAR system for license plate detection and recognition. Pattern Recognition Letters, 129, 213-223.

Singh, J., & Bhushan, B. (2019). Real Time Indian License Plate Detection using Deep Neural Networks and Optical Character Recognition using LSTM Tesseract. 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS),

Wang, D., Tian, Y., Geng, W., Zhao, L., & Gong, C. (2020). LPR-Net: Recognizing Chinese license plate in complex environments. Pattern Recognition Letters, 130, 148-156.

Yu, M., & Kim, Y. D. (2000). An approach to Korean license plate recognition based on vertical edge matching. Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.'cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0,

Yu, S., Li, B., Zhang, Q., Liu, C., & Meng, M. Q.-H. (2015). A novel license plate location method based on wavelet transform and EMD analysis. Pattern Recognition, 48(1), 114-125.




How to Cite

Hassan, A. M., Ghoul, S. A. ., & Alkabir , A. A. . (2022). Libyan Vehicle License Plate Recognition with Support Vector Machine. Al-Mukhtar Journal of Sciences, 37(1), 1–13.



Research Articles