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All Journal EMITTER International Journal of Engineering Technology
Zaid Abdi Alkareem
Information Technology Research and Development Center (ITRDC) University of Kufa Najaf, Iraq
Computer Science & IT

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Accurate Automatic Recognition of Iraqi License Plates Using YOLOv11n and Enhanced OCR Techniques Aymen saad; Usman Ullah Sheikh; Zaid Abdi Alkareem
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.999

Abstract

Automatic License Plate Recognition (ALPR) systems are widely utilized for traffic monitoring, law enforcement, and security applications. While previous ALPR techniques have demonstrated high accuracy on uniform license plate formats, recognizing license plates in many countries particularly in Arabic-speaking regions remains a significant challenge due to the complex structure and diverse designs of the plates. Moreover, visual similarities between certain English letters and Arabic numerals often lead to misinterpretations by Optical Character Recognition (OCR) systems. This study proposes an efficient ALPR framework tailored for the new Iraqi license plates, employing image processing and deep learning (DL) techniques. The system integrates the YOLOv11n object detection model for accurate license plate localization, and OCR for character recognition. To address the OCR misclassification issue, particularly those caused by Arabic-English character similarities, the OCR component is enhanced using Character Index Checking (CCI) and Regular Expression Patterns (REP), resulting in more stable and accurate recognition outputs. The proposed method was evaluated on a real dataset of Iraqi license plates under varying illumination, viewpoints, and background complexity. Experimental results demonstrate strong end-to-end ALPR performance, achieving 100% plate detection accuracy, 99.8% recognition recall, and 99.5% mAP@0.5, confirming the robustness and practical effectiveness of the proposed system.
Co-Authors Aymen saad Usman Ullah Sheikh