# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 2 Related Work  
There are many recent studies that demonstrate the current interest in applying deep learning techniques for ALPR. Researchers from various parts of the world have conducted studies using different approaches and datasets. The datasets used in these studies are either public or collected by the researchers themselves and contain license plates from various countries. The following section briefly presents the approaches adopted by other authors in similar studies. The work [7] presents an automatic license plate recognition system that uses grayscale images, edge detection algorithms, the Radon transform for tilt correction, and classification based on template matching. The proposed system was tested on 1000 images of Israeli and Bulgarian license plates. It correctly detected 81.2% of the plates and recognized 85.2% of the characters. The performance of the proposed system may be affected by poor lighting, reflections, or partially obstructed plates, and extending the system to other license plate formats requires additional adaptations. In the paper [12], the authors propose an automatic vehicle license plate detection system from images, in three steps. The first step consists of license plate detection, the second step consists of a segmentation operation that determines the character boundaries within the detected plate regions, and the third step consists of character recognition. The experimental results were performed using an Iranian dataset composed of 159 images, containing 379 license plates, which was augmented. Noise filtering in the images was performed using a mean filter that replaces the value of each pixel with the average value of its neighboring pixels. To improve the execution time of the proposed method, a parallel implementation of the mean filter was developed on a GPU platform using CUDA. The proposed solution led to speedups of up to 14.54\(\times\) at the kernel level and 10.77\(\times\) at the application level. Approximately 96.16% of the plates were detected. In [8], the authors developed an ALPR system based on YOLOv8 and OCR for the recognition of Egyptian license plates. The model was trained and tested using a public dataset. ALPR was performed in two phases: in the first phase, the license plate was detected using YOLOv8, and in the second phase, the text on the plate was detected using two approaches. The first approach uses Easy-OCR, and the second approach uses a custom CNN model trained on Arabic characters. The combination of YOLOv8 and the custom CNN resulted in a detection accuracy of 99.4%. In the work [11], the authors use a public dataset consisting of over 4000 images of vehicles with German license plates. The study employs YOLOv8 for detecting the location of the plate in the image and EasyOCR for recognizing the text on the plates, achieving an accuracy of 95% for plate localization and a text recognition rate of 94%. The model was trained for up to 1000 epochs. In the study [13], the authors propose a convolutional neural network framework that improves license plate detection under adverse weather conditions (rain, fog, snow, pollution). The study uses a dataset of vehicles registered in Bangladesh, which was augmented to simulate low-visibility weather conditions. In this work, the authors build detection models for all six variants of the YOLOv10 algorithm (n, s, m, b, l, x), using both the augmented and non-augmented datasets. The maximum detection accuracy achieved is 97.1%. The authors of [14] performed an ALPR study using a dataset containing 2231 images collected from highways and toll roads in Semarang, Indonesia. The proposed solution first detects the windshield, beneath which lies the Region of Interest (ROI) containing the license plate. The YOLOv11 model is used for windshield detection, for license plate detection within the ROI, and for character detection on the plates. Windshield detection can also be used in complementary studies to determine whether front-seat passengers are wearing seat belts.