Arabic and Latin license plate detection and recognition based on YOLOv7 and image processing methods

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Abstract License plate detection has always been challenging, especially with the increasing number of smart radars on roads and highways. By 2021, over 1 billion smart traffic radars have been provided worldwide for the gendarmerie as well as for road control officers from the National Security. This paper introduces a novel Arabic and Latin license plate detection and recognition method. In the first stage of the proposed method and after gathering images to build a new dataset, we used YOLOv7 to detect and localize the license plate in the image. The dataset has been labeled manually before feeding it to the detection system. Second, we applied some of the machine learning algorithms to enhance the detected license plate. For this, we employed thresholding as well as the kernel algorithms in order to remove the additional vertical lines in the plate. Afterward, we used the Arabic OCR as well as Easy OCR techniques to recognize the Arabic and the Latin letters on the license plate. The proposed detection algorithm achieved a precision of 97% and a recall of 98%, consequently, we achieved an F1 score of 98% in license plate detection. For image segmentation, while using Arabic OCR and Easy OCR to segment and extract characters from the detected license plate, we achieved an accuracy of 99 %. The proposed method gave satisfactory results for both detection and segmentation.

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