Optimized Python Approach for Vehicle License Plate Detection and Recognition

Abstract—Automatic license plate detection and recognition (ALPR) is a critical component of intelligent transportation systems, enabling applications such as traffic monitoring, parking management, and law enforcement. This paper presents an efficient Python-based approach that integrates image processing, computer vision, and optical character recognition (OCR) techniques for real-time license plate detection and recognition. The proposed framework employs pre-processing methods to enhance image quality, followed by contour detection and segmentation to accurately localize license plates. Subsequently, character recognition is performed using OCR to extract alphanumeric information from the detected plates. The system is evaluated on diverse datasets containing vehicles under varying lighting and environmental conditions. Experimental results demonstrate high detection accuracy, precision, recall, and F1-score while maintaining low computational overhead, making it suitable for deployment in real-world scenarios. The Python-based implementation ensures flexibility, scalability, and ease of integration with existing IoT and smart city infrastructures.

ALPR, Python, Computer Vision, Image Processing, Optical Character Recognition, Vehicle Identification, Intelligent Transportation Systems

1. I. Valova, T. Kaneva and N. Valov, "Conceptual Model of a Parking System with Automatic License Plate Recognition," 2024 IEEE 30th International Symposium for Design and Technology in Electronic Packaging (SIITME), Sibiu, Romania, 2024, pp. 168-173, doi: 10.1109/SIITME63973.2024.10814907. [Google Scholar] [Crossref]

2. A. Dayal, A. Porwal, A. Gupta, A. K. Srivastava and M. Mahendru, "Automatic Number Plate Recognition System using Machine Learning Techniques," 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), Greater Noida, India, 2024, pp. 1-4, doi: 10.1109/ICEECT61758.2024.10739064. [Google Scholar] [Crossref]

3. X. Du, H. Chen, J. Huan and Y. Du, "License Plate Recognition System Based on Embedded Technology and YOLOv11," 2025 5th International Symposium on Computer Technology and Information Science (ISCTIS), Xi'an, China, 2025, pp. 868-871, doi: 10.1109/ISCTIS65944.2025.11065377. [Google Scholar] [Crossref]

4. K. DC, M. P. Blessy, M. Premalatha and S. Pooja, "Single Shot Detector Based MobileNet for Automatic Detection and Recognition of License Plates," 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), Shivamogga, India, 2024, pp. 1-6, doi: 10.1109/AMATHE61652.2024.10582122. [Google Scholar] [Crossref]

5. T. K. Vashishth, V. Sharma, B. Kumar, S. Chaudhary, R. Panwar and K. K. Sharma, "Guarding Finances: The Role of Image Processing in Unmasking Counterfeit Currency," 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India, 2024, pp. 668-671, doi: 10.1109/InCACCT61598.2024.10551141. [Google Scholar] [Crossref]

6. A. Agarwal, D. Samal, L. Samal, R. K. Nayak and S. N. Mehta, "An Intelligent Video Based Automatic License Plate Detection and Analysis Using Easy OCR and Tensorflow," 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), Bhubaneswar, India, 2025, pp. 1-6, doi: 10.1109/ISAC364032.2025.11156342. [Google Scholar] [Crossref]

7. S. N. Rafek, S. N. K. Kamarudin and Y. Mahmud, "Deep Learning-Based Car Plate Number Recognition (CPR) in Videos Stream," 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), Bangkok, Thailand, 2024, pp. 538-543, doi: 10.1109/AiDAS63860.2024.10730590. [Google Scholar] [Crossref]

8. P. C. S. Reddy, P. Shirley Muller, S. N Koka, V. Sharma, N. Sharma and S. Mukherjee, "Detection of Encrypted and Malicious Network Traffic using Deep Learning," 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), Ballari, India, 2023, pp. 1-6, doi: 10.1109/AIKIIE60097.2023.10390386. [Google Scholar] [Crossref]

9. I. Pallempati, D. V. Lakshmi and M. S. Das, "Handwritten Character Recognition and Vehicle Number Recognition using OCR Method," 2024 Third International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON), Bangalore, India, 2024, pp. 41-45, doi: 10.1109/TEECCON64024.2024.10939189. [Google Scholar] [Crossref]

10. N. Thamilarasi, B. Shanmathi, S. Kannagi, G. M, S. K. M and S. P, "Development of an AI-Based Smart Traffic Guard System for Real-Time Detection and Automated Penalty Enforcement of Traffic Violations," 2025 6th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2025, pp. 804-809, doi: 10.1109/ICIRCA65293.2025.11089648. [Google Scholar] [Crossref]

11. K. V. S. S. R. Krishna, P. Rajyalakshmi, K. L. Asritha, M. Divyasri and K. S. Chaitragna, "Vehicle Number Plate Detection using Deep Learning," 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2024, pp. 1-5, doi: 10.1109/ICICACS60521.2024.10498324. [Google Scholar] [Crossref]

12. D. P. Sofiykov and P. V. Georgieva, "A Computer Vision System for Real-Time Vehicular Traffic Management," 2025 MIPRO 48th ICT and Electronics Convention, Opatija, Croatia, 2025, pp. 63-68, doi: 10.1109/MIPRO65660.2025.11131789. [Google Scholar] [Crossref]

13. K. D. Ismael and E. A. Saeed, "Controlling Mobile Robot Navigation Equipped with Computer Vision Perception for Text using OCR Technique," 2024 21st International Multi-Conference on Systems, Signals & Devices (SSD), Erbil, Iraq, 2024, pp. 1-7, doi: 10.1109/SSD61670.2024.10549002. [Google Scholar] [Crossref]

14. V. Sharma, T. K. Vashishth, S. Kumar, R. Kumar, S. Chaudhary and S. Vidyant, "Next-Gen Child Search: Advanced Predictions with Facial Features and Skin Color," 2025 Seventh International Conference on Computational Intelligence andCommunication Technologies (CCICT), Sonepat, India, 2025, pp. 551-556, doi: 10.1109/CCICT65753.2025.00089. [Google Scholar] [Crossref]

15. J. Kharatmol, N. Visawadia, T. Gurav, S. Daulatkar and K. Dhargalkar, "Extracting Meaningful Data from Education Credentials Using OCR and Image Processing," 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), Delhi, India, 2025, pp. 01-06, doi: 10.23919/INDIACom66777.2025.11115713. [Google Scholar] [Crossref]

16. V. Sharma and S. Teotia, “Optimization of Graph Neural Networks for Real-Time Intrusion Detection in Dynamic Mobile Ad-Hoc Networks,” International Journal of Environmental Sciences, vol. 11, no. 11s, pp. 740–748, Jun. 2025, doi: 10.64252/79452g17. [Google Scholar] [Crossref]

17. V. Sharma, A. Yadav, M. Kumar, S. Kumar, S. Kumar, and J. Singh, “A Comparative Study of Deep Learning Models for Fake News Classification,” International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 14, no. 9, pp. 188–195, 2025, doi: 10.51583/IJLTEMAS.2025.1409000026. [Google Scholar] [Crossref]

18. V. Sharma and S. Teotia, “A Comprehensive Analysis of Security Mechanisms and Threat Characterization in Mobile Ad Hoc Networks,” International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 14, no. 5, pp. 732–737, May 2025. [Google Scholar] [Crossref]

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