An Efficient Layout Index Characters for Automatic License Plate Recognition System Based on the YOLO-v8 Detector

Automatic License Plate Recognition (ALPR) systems have been widely used for traffic management, law enforcement, and security. Previous ALPR methods have achieved impressive performance on single-type license plates. However, multi-type license plate recognition is still challenging due to various character layouts and fonts. This study presents an efficient ALPR system based on the YOLO-V8 object detector and optical character recognition (OCR) to read the license plate (LP) content. OCR sometimes misinterprets certain English letters as Arabic numbers and certain Arabic numbers as English letters because of their visual similarities. Therefore, this study has improved the OCR to improve recognition results by including the checking character index (CCI) stage to fine-tune the detection results. The proposed ALPR uses YOLO-v8 and was evaluated using real Iraqi license plate datasets and achieved a precision and recall of $100 \%$, and $\mathbf{9 9 . 8 \%}$ respectively, with mAP confidence curve values of 99.5.