Intelligent license Plate Recognition System
Keywords:
Vehicles, Lp, Monitoring, Ilpr, Identity, Parking ControlAbstract
Since the 19th century, the number of vehicles has been increasing rapidly with the growth of the human population. To supervise vehicles, license plates are used all over the world. The license plate is the unique identity for vehicles; that’s why it is always used to monitor and keep records of vehicles by law enforcement, border monitoring, parking control, and many other applications. Monitoring a huge number of vehicles is a difficult task using traditional (manual) methods. The Intelligent License Plate Recognition (ILPR) system overcomes these problems by recognizing plate identities without human involvement through artificial intelligence and machine learning processes. This system extracts the identity number allocated to each vehicle from the license plate and can provide information about a specific vehicle. It can be further applied in regulated zones such as military areas, parking control, toll collection, and for identifying non-tax-paid vehicles. For developing the ILPR system, text extraction and deep learning techniques must be combined.
The ILPR system, developed by integrating Deep Learning (DL), Image Processing (IP), and image-to-text extraction approaches, is used to detect plate identity. YOLOv8 is used for object detection and the OCR engine for text extraction. The system will be capable of detecting live license plates with high accuracy, which will help in regulated zones and traffic system applications.
References
S. Mehtab and J. Sen, “Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models,” 2020 Int. Conf. Decis. Aid Sci. Appl. DASA 2020, pp. 447–453, Nov. 2020, doi: 10.1109/DASA51403.2020.9317207.
H. M. Hanxiang Wang, Yanfen Li, L.-Minh Dang, “Robust Korean License Plate Recognition Based on Deep Neural Networks,” Sensors, vol. 21, no. 12, p. 4140, 2021, doi: https://doi.org/10.3390/s21124140.
Hoanh Nguyen, “An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks,” Adv. Multimed., 2022, doi: https://doi.org/10.1155/2022/8852142.
M. T. Javier Villena Toro, Anton Wiberg, “Optical character recognition on engineering drawings to achieve automation in production quality control,” Front. Manuf. Technol, vol. 3, 2023, doi: https://doi.org/10.3389/fmtec.2023.1154132.
S. Shah, N. Rathod, P. K. Saini, V. Patel, H. Rajput, and P. Sheth, “Automated Indian Vehicle Number Plate Detection,” Adv. Intell. Syst. Comput., vol. 742, pp. 453–461, 2019, doi: 10.1007/978-981-13-0589-4_42.
L. G. C. Hamey and C. Priest, “Automatic Number Plate Recognition for Australian Conditions,” Digit. Image Comput. Tech. Appl. (DICTA’05), Queensland, Aust., pp. 14–14, Oct. 2006, doi: 10.1109/DICTA.2005.17.
H. N. Tran and T. Nguyen, “Supervised learning method for mobile international number plate recognition,” ACM Int. Conf. Proceeding Ser., vol. 04-06-December-2014, pp. 212–216, Dec. 2014, doi: 10.1145/2684200.2684307;PAGEGROUP:STRING:PUBLICATION.
W. W. Keong and V. Iranmanesh, “Malaysian automatic number plate recognition system using Pearson correlation,” ISCAIE 2016 - 2016 IEEE Symp. Comput. Appl. Ind. Electron., pp. 40–45, Sep. 2016, doi: 10.1109/ISCAIE.2016.7575034.
O. Hommos, A. Al-Qahtani, A. F. A. Al-Zawqari, F. Bensaali, A. Amira, and X. Zhai, “HD Qatari ANPR system,” 2016 Int. Conf. Ind. Informatics Comput. Syst. CIICS 2016, Apr. 2016, doi: 10.1109/ICCSII.2016.7462420.
Y. P. Huang, S. Y. Lai, and W. P. Chuang, “A template-based model for license plate recognition,” Conf. Proceeding - IEEE Int. Conf. Networking, Sens. Control, vol. 2, pp. 737–742, 2004, doi: 10.1109/ICNSC.2004.1297038.
B. Diaz Acosta, “Experiments in Image Segmentation for Automatic US License Plate Recognition,” Virginia Tech, 2004, [Online]. Available: https://vtechworks.lib.vt.edu/items/88b162d8-5691-42d5-a31a-6f0a3f46613c
S. M. M. K. and S. A. M. S.H. Mohades Kasaei, “A Novel Morphological Method for Detection and Recognition of Vehicle License Plates,” Am. J. Appl. Sci., vol. 6, no. 12, pp. 2066–2070, 2009, [Online]. Available: https://www.thescipub.com/abstract/10.3844/ajassp.2009.2066.2070
P. Dhar, M. Z. Abedin, R. Karim, Fatema-Tuj-Johora, and M. S. Hossain, “Bangladeshi license plate recognition using adaboost classifier,” 2019 Jt. 8th Int. Conf. Informatics, Electron. Vision, ICIEV 2019 3rd Int. Conf. Imaging, Vis. Pattern Recognition, icIVPR 2019 with Int. Conf. Act. Behav. Comput. ABC 2019, pp. 342–347, May 2019, doi: 10.1109/ICIEV.2019.8858580.
S. A. Haider and K. Khurshid, “An implementable system for detection and recognition of license plates in Pakistan,” ICIEECT 2017 - Int. Conf. Innov. Electr. Eng. Comput. Technol. 2017, Proc., May 2017, doi: 10.1109/ICIEECT.2017.7916553.
N. Saleem, H. Muazzam, H. M. Tahir, and U. Farooq, “Automatic license plate recognition using extracted features,” 2016 4th Int. Symp. Comput. Bus. Intell. ISCBI 2016, pp. 221–225, Nov. 2016, doi: 10.1109/ISCBI.2016.7743288.
J. A. Khan, M. A. Shah, A. Wahid, M. H. Khan, and M. B. Shahid, “Enhanced car number plate recognition (ECNPR) system by improving efficiency in preprocessing steps,” Int. Conf. Commun. Technol. ComTech 2017, pp. 156–161, Oct. 2017, doi: 10.1109/COMTECH.2017.8065766.
B. Sachin Prabhu, S. Kalambur, and D. Sitaram, “Recognition of Indian License Plate number from live stream videos,” 2017 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2017, vol. 2017-January, pp. 2359–2365, Nov. 2017, doi: 10.1109/ICACCI.2017.8126199.
J. Singh and B. Bhushan, “Real Time Indian License Plate Detection using Deep Neural Networks and Optical Character Recognition using LSTM Tesseract,” Proc. - 2019 Int. Conf. Comput. Commun. Intell. Syst. ICCCIS 2019, vol. 2019-January, pp. 347–352, Oct. 2019, doi: 10.1109/ICCCIS48478.2019.8974469.
S. H. Kasaei, S. M. Kasaei, and S. A. Kasaei, “New Morphology-Based Method for RobustIranian Car Plate Detection and Recognition,” Int. J. Comput. Theory Eng., pp. 264–268, 2010, doi: 10.7763/IJCTE.2010.V2.150.
S. L. Prasun Gokhlani, Ridhi Jhamb, “Automatic Vehicle Identification by Plate Recognition,” Int. Res. J. Eng. Technol., vol. 8, no. 7, 2021, [Online]. Available: https://www.irjet.net/archives/V8/i7/IRJET-V8I7128.pdf
M. Pradyut Agrawal, Akshansh Jha, Ravneet Kaur, Anju Agrawal and Bhattacharya, “Automatic License Plate Detection and Recognition using Deep Learning and Image Processing,” Div. Electron., 20214, [Online]. Available: https://ceur-ws.org/Vol-3682/Paper1.pdf
S. U. Rehman, M. Ahmad, A. Nawaz, and T. Ali, “An Efficient Approach for Vehicle Number Plate Recognition in Pakistan,” AACE Clin. Case Reports, vol. 7, no. 1, p. 1, Jun. 2021, doi: 10.2174/1874061802006010012.
M. T. B. Salma, Maham Saeed, Rauf ur Rahim, Muhammad Gufran Khan, Adil Zulfiqar, “Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR,” Complexity, 2021, [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1155/2021/5597337
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 50sea
This work is licensed under a Creative Commons Attribution 4.0 International License.
