Plate Number Recognition based on Hybrid Techniques


  • Hawar Hussein Yaba Computer Institute in Sulaymaniyah, Sulaymaniyah, Iraq
  • Hemin Omer Latif Department of IT/SE, American University of Iraq, Sulaimani, Sulaymaniyah, Iraq



Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.


I. N. Mahmood, H. T. S. AlRikabi, A. H. M. Alaidi and F. T. Abed. “Design and Implementation of a Smart Traffic Light Management System Controlled” Wirelessly By Arduino, 2019.

Y. Kessentini, M. D. Besbes, S. Ammar and A. Chabbouh. “A two-stage deep neural network for multi-norm license plate detection and recognition”. Expert Systems with Applications, vol. 136, pp. 159-170, 2019.

S. Sugeng and E. Y. Syamsuddin. “Designing automatic number plate recognition (ANPR) systems based on K-NN machine learning on the raspberry pi Embedded system”. JTEV Journal Teknik Elektro dan Vokasional. vol. 5, pp. 19-26, 2019.

Q. Ying and J. G. Sheng. “Research of License Plate Recognition Based on HSV Space”. 3rd International Conference on Materials Engineering, Manufacturing Technology and Control. Atlantis Press, 2016.

A. R. Quiros, R. A. Bedruz, A. C. Uy, A. Abad, A. Bandala, E. Dadios and A. Fernando. “A kNN-based Approach for the Machine Vision of Character Recognition of License Plate Numbers”. TENCON-IEEE Region 10 Conference, pp. 1081-1089, 2017.

M. Khan, M. Hassan, A. B. M. Noman and S. Rajbangshi. “Language Recognition and Translation from Document”, 2018.

A. Al-Zawqari, O. Hommos, A. Al-Qahtani, A. A. Farhat, F. Bensaali, X. Zhai and A. Amira. “HD number plate localization and character segmentation on the Zynq heterogeneous SoC”. Journal of Real- Time Image Processing, vol. 16, pp. 1-15, 2018.

E. Alpaydin. “Introduction to Machine Learning”. MIT Press, Cambridge, 2014.

M. Mohri, A. Rostamizadehand and A. Talwalkar. “Foundations of Machine Learning”. MIT Press, Cambridge, 2018.

P. Lison. “An Introduction to Machine Learning”. Springer, Cham, Denmark, 2015.

M. Kuhn and K Johnson. Applied Predictive Modelling”. Vol. 26, Springer, New York, 2013.

T. Hastie, R. Tibshiraniand and J. Friedman. “The Elements of Statistical Learning”. Springer Series in Statistics, Springer, New York, 2001.

J. O. Rawlings, S. G. Pantula and D. A. Dickey. Applied Regression Analysis: A Research Tool. Springer Science and Business Media, Germany, 2001.

T. K. Hazra, D. P. Singh, and N. Daga. Optical Character Recognition using KNN on Custom Image Dataset. In: 8th Annual Industrial Automation and Electromechanical Engineering Conference, pp. 110-114, 2017

N. B. A. Hamid and N. N. B. Sjarif. “Handwritten Recognition Using SVM, KNN and Neural Network”. arXiv, vol. 2017, p. 00723.

D. AbdAlhamza and A. Alyathawy. “Iraqi license plate number recognition based on machine learning”. Iraqi Journal of Information and Communications Technology, vol. 3, pp. 2222-758x, 2020.