# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 3 Data Preparation and Technical Resources
### Model Used  
For the license plate detection task, the YOLO model was used, in its most recent available version, YOLOv12 [20; 25]. The YOLOv12 model was released on 18 February 2025, marking a new stage in the evolution of object detection architectures. The YOLOv12 architecture combines feature extraction through CNNs with an advanced attention system, optimized for fast detection tasks. Due to a balanced design between complexity and performance, the model proves efficient in real-time applications, ensuring accurate detection without high computational costs. Table 1[25] presents the performance results of the different YOLOv12 model variants--nano (n), small (s), medium (m), large (l), and extra-large (x)--evaluated on the COCO val2017 validation set [25]. The following metrics are included: [EMAIL_ADDRESS]:0.95, which represents the mean of the Average Precision (AP) values calculated at ten IoU overlap thresholds (from 0.5 to 0.95, in steps of 0.05), inference time, model size, and computational complexity. To evaluate the performance and technical characteristics of the YOLOv12 variants, a comparative analysis of the main performance metrics is conducted. The following section highlights the relationships between detection accuracy, model complexity, and inference speed across the five model versions (n, s, m, l and x):  
* Resolution (px): All models use the same resolution of 640 px. * [EMAIL_ADDRESS]:0.95: YOLO12x has the highest mean Average Precision (mAP), at 55.2%. * Inference Time (ms): Inference time increases as model complexity increases, from 1.64 ms for YOLO12n up to 11.79 ms for YOLO12x. * Parameters (M): The number of parameters increases from 2.6 M for YOLO12n to 59.1 M for YOLO12x, indicating higher model complexity. * FLOPs (B): Computational cost (FLOPs) grows proportionally with model size, from 6.5 B for YOLO12n to 199.0 B for YOLO12x. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Resolution (px)** & **[EMAIL_ADDRESS]:0.95** & **Time (T4)** & **Params (M)** & **FLOPs (G)** & **Comparison (mAP/speed)** \\ \hline YOLO12n & 640 & 40.6 & 1.64 ms & 2.6 & 6.5 & +2.1\%/\(-\)9\% vs. YOLOv10n \\ YOLO12s & 640 & 48.0 & 2.61 ms & 9.3 & 21.4 & +0.1\%/\(+\)42\% vs. RT-DETRv2 \\ YOLO12m & 640 & 52.5 & 4.86 ms & 20.2 & 67.5 & +1.0\%/\(-\)3\% vs. YOLOv11m \\ YOLO12l & 640 & 53.7 & 6.77 ms & 26.4 & 88.9 & +0.4\%/\(-\)8\% vs. YOLOv111 \\ YOLO12x & 640 & 55.2 & 11.79 ms & 59.1 & 199.0 & +0.6\%/\(-\)4\% vs. YOLOv11x \\ \hline \hline \end{tabular}
\end{table} 
Table 1: YOLOv12 models [25]—performance detection comparison. Figure 4: YOLO dataset structure and configuration workflow. * Comparison: YOLOv12 shows improvements in mAP compared to YOLOv10, YOLOv11, and RT-DETRv2, but with some trade-offs in inference speed. Figure 5 illustrates the performance of three successive versions of the YOLO (You Only Look Once) architecture--YOLOv10, YOLOv11, and YOLOv12--based on inference time (Latency T4, expressed in milliseconds) and mean accuracy ([EMAIL_ADDRESS]:0.95), using the COCO validation dataset. The \(X\) axis represents the inference time (latency) on the NVIDIA T4 GPU, while the \(Y\) axis shows the [EMAIL_ADDRESS]:0.95 score:  
* YOLOv10 (blue line): This provides a decent balance between speed and accuracy. Latency ranges from 1.84 ms to 10.7 ms, while [EMAIL_ADDRESS]:0.95 increases from 38.5% to 54.4%;
* YOLOv11 (orange line): This brings significant improvements over YOLOv10, with a clear increase in [EMAIL_ADDRESS]:0.95 at comparable latency levels. For example, at 4.7 ms, YOLOv11 achieves 51.5% [EMAIL_ADDRESS]:0.95 compared to 46.3% for YOLOv10;
* YOLOv12 (green line): This delivers the best results among all analyzed versions. For the same inference time, YOLOv12 attains higher mAP values, indicating greater architectural efficiency. For instance, at 4.86 ms, YOLOv12 reaches 52.5%, approximately +1% above YOLOv11 at the same latency. In this study, for the task of license plate detection, the YOLOv12s model was chosen.