International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Video Sensor-Based Automatic License Plate Recognition of Static and Moving Vehicles

Md. Yusuf AliLecturer, Dept. of Civil Engineering, Ahsanuallah University of Science and Technology (AUST), Dhaka-1208, BangladeshNazmul HaqueLecturer, Accident Research Institute (ARI), Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, BangladeshSumaiya Afrose SumaLecturer, Dept. of Civil Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, BangladeshMd. HadiuzzamanProfessor, Dept. of Civil Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, Bangladesh

Vol 6 No 8 (2022): Volume 6, Issue 8, August 2022 | Pages: 25-30

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 13-08-2022

doi Logo doi.org/10.47001/IRJIET/2022.608004

pdf iconFull Text PDF
Abstract

In Bangladesh, present transportation system needs suitable control schemes to ensure safety and mobility to the road users. License Plate Recognition (LPR) is one of the control schemes which can be used to ensure road safety using video sensors. Moreover, from installation point of view, it is cheaper than Radiofrequency Identification (RFID) system. Automated LPR is adopted to reduce human involvement which causes loss in accuracy and reliability in reading license plate. The extracted information from LPR can be used in various schemes, such as electronic payment system, traffic surveillance and extracting vehicle trajectory. In this paper, we are presenting an offline dynamic LPR system, which uses geometric properties of Bengali alphabets stored in alphabet template database to recognize license plates from video. The system comprises of three main components: 1) License Plate Locating (LPL), 2) Character Segmentation (CS), and 3) Geometric Character Recognition (GCR). In this regard, the image is converted into a binary image considering morphological operations and color modification. A new ratio-based detection algorithm has been proposed for the segmentation of license plate. Finally, the alphabet template database and the character extracted from detected license plate are correlated to recognize alphanumeric character which deduces the plate number. Thus, the plate number can be utilized for further action.

Keywords

Automated license plate recognition, Bengali alphabet, ratio-based detection, character recognition


Citation of this Article

Md. Yusuf Ali, Nazmul Haque, Sumaiya Afrose Suma, Md. Hadiuzzaman, “Video Sensor-Based Automatic License Plate Recognition of Static and Moving Vehicles”, Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 6, Issue 8, pp 25-30, August 2022. Article DOI https://doi.org/10.47001/IRJIET/2022.608004
Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Amit, Y., Geman, D., and Fan, X. (2004). A coarseto-fine strategy for multiclass shape detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(12), 1606-1621.
  2. Barroso, J., Dagless, E. L., Rafael, A., and Bulas-Cruz, J. (1997, July). Number plate reading using computer vision. In ISIE'97 Proceeding of the IEEE International Symposium on Industrial Electronics (pp. 761-766). IEEE.
  3. Cano, J., and Pérez-Cortés, J. C. (2003, June). Vehicle license plate segmentation in natural images. In Iberian Conference on Pattern Recognition and Image Analysis (pp. 142-149). Springer, Berlin, Heidelberg.
  4. Chang, S. L., Chen, L. S., Chung, Y. C., and Chen, S. W. (2004). Automatic license plate recognition. IEEE transactions on intelligent transportation systems, 5(1), 42-53.
  5. Comelli, P., Ferragina, P., Granieri, M. N., and Stabile, F. (1995). Optical recognition of motor vehicle license plates. IEEE transactions on Vehicular Technology, 44(4), 790-799.
  6. Kahraman, F., Kurt, B., and Gökmen, M. (2003, November). License plate character segmentation based on the gabor transform and vector quantization. In International Symposium on Computer and Information Sciences (pp. 381-388). Springer, Berlin, Heidelberg.
  7. Kocer, H. E., and Cevik, K. K. (2011). Artificial neural networks-based vehicle license plate recognition. Procedia Computer Science, 3, 1033-1037.
  8. Oz, C., and Ercal, F. (2005, June). A practical license plate recognition system for real-time environments. In International Work-Conference on Artificial Neural Networks (pp. 881-888). Springer, Berlin, Heidelberg.
  9. Öztürk, F., and Özen, F. (2012). A new license plate recognition system based on probabilistic neural networks. Procedia Technology, 1, 124-128.
  10. Pan, X., Ye, X., and Zhang, S. (2005). A hybrid method for robust car plate character recognition. Engineering Applications of Artificial Intelligence, 18(8), 963-972.
  11. Rafique, S., Iqbal, M., and Habib, H. A. (2009, December). Space invariant vehicle recognition for toll plaza monitoring and auditing system. In 2009, IEEE 13th International Multitopic Conference (pp. 1-6). IEEE.
  12. Rahati, S., Morvejian, R., Kazemi E. M., and Kazem, F. M., “Vehicle Recognition Using Contourlet Transform and SVM,” Proceedings of the Fifth International Conference on Information Technology, 2008.
  13. Roy, A., and Ghoshal, D. P. (2011, March). Number Plate Recognition for use in different countries using an improved segmentation. In 2011 2nd National Conference on Emerging Trends and Applications in Computer Science (pp. 1-5). IEEE.
  14. Sotelo, M. A., Nuevo, J., Bergasa, L. M., Ocana, M., Parra, I., and Fernández, D. (2005, June). Road vehicle recognition in monocular images. In Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005. (Vol. 4, pp. 1471-1476). IEEE.
  15. Wang, W. (2009, April). Reach on Sobel operator for vehicle recognition. In 2009 International Joint Conference on Artificial Intelligence (pp. 448-451). IEEE.
  16. Wen, Y., Lu, Y., Yan, J., Zhou, Z., von Deneen, K. M., and Shi, P. (2011). An algorithm for license plate recognition applied to intelligent transportation system. IEEE Transactions on intelligent transportation systems, 12(3), 830-845.
  17. Zhai, X., and Bensaali, F. (2013, November). Standard Definition ANPR System on FPGA and an Approach to Extend it to HD. In 2013 7th IEEE GCC Conference and Exhibition (GCC) (pp. 214-219). IEEE.
  18. Zhai, X., Bensaali, F., and Sotudeh, R. (2012, July). OCR-based neural network for ANPR. In 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings (pp. 393-397). IEEE.
  19. Zunino, R., and Rovetta, S. (2000). Vector quantization for license-plate location and image coding. IEEE Transactions on Industrial Electronics, 47(1), 159-167.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.