# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 1 Introduction  
Automatic license plate recognition (ALPR) from images covers a wide range of applications across various domains, such as vehicle charging and parking systems (intelligent parking management systems [1, 2, 3]), safety and public order applications (detection of stolen vehicles [4], vehicles without valid technical inspection, without mandatory car insurance, without vignettes [5], vehicles that exceeded the speed limit, vehicles that ran red lights, etc. ), restricted area access control applications, and so on. ALPR encounters challenges such as low-lighting conditions [6], the angle at which the vehicle appears in the image [7], obstruction of the license plate by insects, dirt, and the different formats in whichlicense plates occur in various countries [8; 9]. There are a lot of variations in license plate formats across different countries in terms of size, orientation, location, font, style, color, language, etc. [10]. Therefore, many recent studies on automatic license plate recognition were performed using datasets with vehicles registered in Germany [11], Egypt [8], Israel and Bulgaria [7], Iran [12], Bangladesh [13], Indonesia [14], etc. There are also variations in the format of these license plates within countries. For example, German plate formats vary depending on the type of vehicle and the region [11]. The Romanian license plates that were used in this study vary in size depending on the type of vehicle, vary in alphanumeric structure and vary in font color (black, red, green, blue) [15]. According to the authors' current knowledge, the format of Romanian license plates shares common characteristics with the license plate formats of other countries around the world, but their structure, which includes a specific county code followed by a combination of digits and letters, is not identically found in other countries. Therefore, the format of Romanian license plates is unique in terms of its specific combination of elements. Recent approaches focus on the use of deep learning techniques to perform ALPR [8; 9; 10; 11; 13; 14; 16]. Convolutional Neural Networks (CNNs) are successfully used in deep learning for image processing [13]. CNNs learn to perform ALPR through examples, which is why the license plate format used during training is essential. YOLO (You Only Look Once) is an object detection algorithm based on a CNN architecture. YOLO is part of deep learning, uses CNN as its foundation, and is optimized for speed and accuracy in identifying objects in images. The YOLO algorithm has become a reference standard in object detection due to its real-time efficiency and simple design [8]. Different versions of the YOLO algorithm have been successfully applied in many ALPR studies. Thus, the works [17; 18] use YOLOv2, the work [19] uses YOLOv3, the work [5] uses YOLOv5, the work [1] uses YOLOv6, the works [8; 11] use YOLOv8, the work [13] uses six versions of the YOLOv10 algorithm (n, s, m, b, l, x), the work [14] uses YOLOv11, etc. YOLOv12 proposes an architecture focused on attention mechanisms, integrated with CNN components from previous versions of the model, thus managing to maintain the inference speed required for real-time applications. Through methodological innovations in attention mechanisms and network structure, the model achieves top accuracy in object detection without compromising performance [20]. The constant release of new YOLO versions, more and more performant in terms of accuracy and speed, imposes the periodic renewal and testing of existing systems in order to take advantage of the improvements brought and to maintain performance at a competitive level. In the context presented above, the current work proposes an ALPR study using the latest version of the YOLO algorithm, namely YOLOv12, which, according to the authors' current knowledge, has not yet been used in other ALPR studies. The study uses a dataset containing 744 images of vehicles registered in Romania.