## 5 Conclusions  
In this paper, we developed a license plate recognition system for unconstrained environments and proposed a novel method that can effectively recognize license plates with large angular distortions. The developed license plate recognition system comprises vehicle and license plate detection, license plate corner detection, license plate correction, and text recognition stages. In the proposed system, we significantly improved the accuracy of license plate corner detection by integrating YOLOv8-based vehicle and license plate detection models with a CEM based on DCNs and parallel ensemble processing techniques utilizing various aspect ratios (original, 2:1, 1.5:1). Through ablation studies, we quantitatively confirmed the effectiveness of the parallel ensemble processing technique. Comparing the baseline CSPNext model (AP 0.608) with the CSPNext + Ensemble model (AP 0.692), the ensemble processing alone achieved approximately a 13.8% performance improvement. The ablation study results also confirmed the critical importance of DCN, where the CEM module with DCN (CSPNext + CEM) achieved an AP of 0.685, while the module without DCN showed an AP of 0.652, representing approximately a 4.8% performance degradation. The final proposed model of CSPNext + CEM + Ensemble achieved significant accuracy improvement from AP 0.608 to 0.721 (approximately 18.6% improvement), while maintaining a real-time processing capability with 32.6 ms latency (approximately 30.7 FPS). This indicates that the corner detection algorithm operated more accurately by effectively correcting the geometric characteristics of distorted license plates through the DCN's adaptive receptive field and various aspect ratio transformations. We verified its applicability in real road environments by building a real-time license plate recognition system using Raspberry Pi 4 and a camera module and establishing an efficient real-time recognition system through a processing structure utilizing cloud servers. However, the current system has the limitation of a somewhat slower processing speed due to increased computational complexity from parallel ensemble processing. Future research will focus on further optimizing the model structure and improving performance under more diverse environmental conditions. Additional research is needed to improve performance in various environmental conditions, such as snow, rain, and fog, and to enhance adaptability to license plates from different countries. In conclusion, the license plate recognition system integrating a DCN-based CEM and the parallel ensemble processing techniques proposed in this study proved to achieve high recognition accuracy even in unconstrained environments, particularly under conditions with large angular distortions. This is expected to be utilized in various applications such as intelligent transportation systems, parking management, and road surveillance. Figure 11: Validation results in parking lot environments: (**a**) Vehicle license plate input image in parking environment; (**b**) Corner detection; (**c**) Homography-based distortion correction; (**d**) License plate recognition based on OCR. **Author Contributions:** Conceptualization, S.K. ; methodology, K.S. ; software, S.K. ; validation, S.K. and K.S. ; formal analysis, S.C.; investigation, J.K.; resources, S.K. ; data curation, K.S. ; writing--original draft preparation, S.K. ; writing--review and editing, S.C., J.K. and K.S. ; visualization, S.C.; supervision, K.S. ; project administration, K.S. ; funding acquisition, Kwangwoon University. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Kwangwoon University. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are publicly available and can be accessed through the Roboflow plaka_tanima Object Detection. Further details regarding the dataset are provided in Section 4.1 of the article.