Automated Recognition of Uzbekistan Automobile License Plates: A Robust ANPR System


  • Asrorbek Eraliev Turin Polytechnic University in Tashkent
  • Jaloliddin Yusupov Turin Polytechnic University in Tashkent


ANPR, Uzbekistan, image processing


In today’s modern world, Automatic Number Plate Recognition (ANPR) systems play a pivotal role in various applications, including law enforcement, traffic management, and security. This paper presents a comprehensive ANPR system specifically tailored for recognizing Uzbekistan automobile plate numbers. The developed model integrates advanced image processing and Optical Character Recognition (OCR) techniques to achieve accurate and efficient license plate recognition specifically the Uzbekistan automobile plate numbers. The system’s versatility is demonstrated through successful testing on static images and live video feeds, showcasing its potential for widespread deployment.


do Vale Dalarmelina, Nicole & Teixeira, Marcio & Meneguette, Rodolfo. (2019). A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS. Sensors (Basel, Switzerland). 20. 10.3390/s20010055.

Y. Shima, "Extraction of number plate images based on image category classification using deep learning," 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), Tokyo, Japan, 2016, pp. 19-26, doi: 10.1109/IRIS.2016.8066060.

Patel, Chirag & Shah, D. & Patel, Atul. (2013). Automatic Number Plate Recognition System (ANPR): A Survey. International Journal of Computer Applications (IJCA). 69. 21-33. 10.5120/11871-7665. DOI: 10.5120/11871-7665

S. Azam and M. Islam, ‘Automatic license plate detection in hazardous condition’, Journal of Visual Communication and Image Representation, Volume 36, Pages 172–186, 2016,

Yousri Kessentini, Mohamed Dhia Besbes, Sourour Ammar, Achraf Chabbouh, A two-stage deep neural network for multi-norm license plate detection and recognition, Expert Systems with Applications, Volume 136, 2019, Pages 159-170, ISSN 0957-4174,

Automatic Number Plate Recognition (ANPR) – 2024 Guide. (accessed Feb. 10, 2024).

Junqing Tang, Li Wan, Jennifer Schooling, Pengjun Zhao, Jun Chen, Shufen Wei, Automatic number plate recognition (ANPR) in smart cities: A systematic review on technological advancements and application cases, Cities, Volume 129, 2022, 103833, ISSN 0264-2751,

Prem, Er & Roy, Prem & Thapa, Arjun & Shrestha, Kumar & Karmacharya, Prasanna & Karna, Rajan. Vehicle Number Plate Recognition and Parking System. International Research Journal of Innovations in Engineering and Technology (IRJIET)ISSN (online): 2581-3048Volume 2, Issue 10, pp 18-23, December-2018

Xianyuan Zhan, Ruimin Li, Satish V. Ukkusuri, Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data, Transportation Research Part C: Emerging Technologies, Volume 117, 2020, 102660, ISSN 0968-090X,

Gabriele Guarnieri, Marco Fontani, Francesco Guzzi, Sergio Carrato, Martino Jerian, Perspective registration and multi-frame super-resolution of license plates in surveillance videos, Forensic Science International: Digital Investigation, Volume 36, 2021, 301087, ISSN 2666-2817,

A. Sasi, S. Sharma and A. N. Cheeran, "Automatic car number plate recognition," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 2017, pp. 1-6, doi: 10.1109/ICIIECS.2017.8275893.

Yonten Jamtsho, Panomkhawn Riyamongkol, Rattapoom Waranusast, Real-time license plate detection for non-helmeted motorcyclist using YOLO, ICT Express, Volume 7, Issue 1, 2021, Pages 104-109, doi:

M. Satsangi, M. Yadav and P. S. Sudhish, "License Plate Recognition: A Comparative Study on Thresholding, OCR and Machine Learning Approaches," 2018 International Conference on Bioinformatics and Systems Biology (BSB), Allahabad, India, 2018, pp. 1-6, doi: 10.1109/BSB.2018.8770662.

Omar, Naaman & Sengur, Abdulkadir & Al-Ali, Salim. (2020). Cascaded Deep Learning-Based Efficient Approach for License Plate Detection and Recognition. Expert Systems with Applications. 149. 113280. 10.1016/j.eswa.2020.113280.

Gao, Jing & Sun, Lijun & Cai, Ming. (2018). Quantifying privacy vulnerability of individual mobility traces: a case study of license plate recognition data.

Z. Selmi, M. Ben Halima and A. M. Alimi, "Deep Learning System for Automatic License Plate Detection and Recognition," 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, 2017, pp. 1132-1138, doi: 10.1109/ICDAR.2017.187.

Palaiahnakote Shivakumara, et al, Fractional means based method for multi-oriented keyword spotting in video/scene/license plate images, Expert Systems with Applications, Volume 118, 2019, Pages 1-19, ISSN 0957-4174,

Runmin Wang, Nong Sang, Ruolin Wang, Liangwei Jiang, Detection and tracking strategy for license plate detection in video, Optik, Volume 125, Issue 10, 2014, Pages 2283-2288, ISSN 0030-4026,

Asrorbek Eraliev, Uktam Salomov. Development of energy efficient WSN based smart monitoring system. 15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI-2023 Proceedings, 2023. DOI: 10.1109/ECAI58194.2023.10194149

Eraliev A., Bracco G. Design and implementation of zigbee based low-power wireless sensor and actuator network (WSAN) for automation of urban garden irrigation systems (2021) 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021. DOI: 10.1109/IEMTRONICS52119.2021.9422568




How to Cite

Eraliev, A., & Yusupov, J. (2024). Automated Recognition of Uzbekistan Automobile License Plates: A Robust ANPR System. Science and Education, 5(6), 51–60. Retrieved from



Technical Sciences