# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 6 Conclusions  
In recent years, there has been a growing interest in implementing ALPR using modern deep learning techniques. As a result, many recent studies have applied deep learning approaches based on datasets containing vehicles registered in various countries (Germany, Egypt, Israel, Bulgaria, etc.). Summaries of selected studies were presented in the Related Work Section. The YOLO algorithm is part of deep learning, uses CNN as a foundation, and is optimized for speed and accuracy in object detection within images. The YOLO algorithm has become a reference standard in object detection. Recently, many versions of the YOLO algorithm have been successfully used in ALPR. In the present work, a YOLOv12 model was trained and evaluated for license plate detection in images. The proposed model demonstrated solid performance both during training and in the testing phase, highlighting stable convergence and a good capacity to adapt to various real-world conditions. Evaluation on external data confirmed its potential for generalization and applicability in diverse practical scenarios. Additionally, the model succeeds in maintaining an effective balance between precision and recall across a range of confidence thresholds, being easy to calibrate according to the specific requirements of the application, from complete identification of objects of interest to minimizing false detections. The main contributions of this research include the construction of a manually labeled dataset, which contains 744 images of vehicles registered in Romania (expanded by augmentation in the training stage to 14,880 images), publicly available on GitHub [21]. Additionally, the source code used for training and evaluation has also been made available to facilitate reuse, verification, and further development by the research community. Also, the proposed system was trained and evaluated for automatic license plate detection in images, using the most recent YOLOv12 model. To the best of the authors' current knowledge, this is the first documented application of the YOLOv12 version for this problem, the obtained results demonstrating superior performance compared to the previous version. Furthermore, the combination of YOLOv12 and PaddleOCR is, as far as we know, unique among the reviewed studies. The novelty element of the research consists in the simultaneous integration of three components: (1) the localization of the license plate in the image, (2) the extraction of the text from it, including the identification of the registration county, specific to Romanian license plates, and (3) the identification of the type of vehicle on which it is mounted. This integrated approach considerably extends the applicability area of ALPR systems, which are usually limited to license plate localization and text extraction. The proposed solution can be used to solve problems such as restricting access of certain types of vehicles to specific areas, monitoring traffic flow based on county, or implementing differentiated policies, such as access or parking charges depending on the type of vehicle or its provenance. A future research direction is the optimization of the proposed system for real-time operation on edge devices, such as smart cameras or embedded units, where local processing can help reduce latency and bandwidth consumption while maintaining high detection accuracy. **Author Contributions:** Conceptualization, B.B. and R.R. ; Data collection and labeling, B.B. ; Software, B.B. ; Writing--original draft, B.B. (Sections 3-5) and R.R. (Abstract, Sections 1, 2 and 6, and References); Writing--review and editing, R.R. and I.F. ; Validation, R.R. and I.F. ; Supervision, R.R. (primary) and I.F. (secondary); Overall guidance, R.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.