## 6 Discussion
These results not only demonstrate the high accuracy and robustness of the model but also statistically confirm that the performance improvements are real and not due to chance. This further enhances the credibility and reliability of our research findings. Our model achieved high accuracy recognition even when faced with a variety of complex traffic conditions, encompassing challenges such as those arising from complex environmental factors, high-speed driving, poor dataset quality, and license plate deformation. The comprehensive approach adopted by our model endows it with robustness to overcome these complexities, marking significant progress in the field of license plate recognition technology. In the field of license plate recognition, the quantity and quality of datasets are crucial for determining recognition accuracy, especially under exceptional conditions. The popular public dataset CCPD, despite its large sample size, primarily consists of license plate data from Anhui Province. However, this dataset has limitations related to sample diversity and practical applicability. In contrast, our custom dataset covers various types of license plates and has been systematically categorized. This systematic approach enables it to accurately represent the model's recognition accuracy in real-world traffic scenarios. The advantage of our dataset lies in its ability to effectively simulate the complex traffic conditions encountered in actual traffic environments. This diversity provides valuable data for rigorous testing and analysis, thereby facilitating comprehensive and objective evaluations of experimental models. Therefore, our dataset represents a significant advancement, offering a more representative and practical foundation for evaluating license plate recognition models under various challenging conditions. Through data analysis, it is evident that experiments conducted on publicly accessible datasets typically yield higher results compared to experiments conducted on custom datasets. This difference indicates that the CMPD dataset accurately reflects the actual recognition accuracy and can depict real traffic situations. The effectiveness of the EAND-LPRM model is apparent in its ability to handle a variety of adverse conditions. Whether it is the more straightforward conditions found in publicly available datasets or the more challenging environments represented in our custom dataset, the model consistently per forms at a high level. This robustness underscores the model's adaptability and reliability in diverse and complex traffic situations, including adverse weather conditions, varying lighting conditions, and different angles of license plate captures. Moreover, the encoder-decoder architecture of our model, combined with the self-attention mechanism, has proven to be particularly effective in recognizing license plates accurately, even when faced with significant distortions or obstructions. This is a notable improvement over traditional methods, as it enables more precise alignment and prediction of license plate characters. The ability of our model to generalize well across different datasets is a testament to its comprehensive design and the thoroughness of the training process. By utilizing a wide range of data that mirror real-world conditions, we have ensured that the EAND-LPRM model can maintain high performance and accuracy in practical applications. This makes it a valuable tool for modern traffic management systems, contributing to the advancement of urban intelligence and enhancing the efficiency of traffic monitoring and enforcement. While the EAND-LPRM model has shown impressive results, there are areas for improvement and further research. One notable challenge is the recognition of double-layer license plates. Double-layer license plates are often used in regions with specific regulatory requirements and can contain additional information such as state identifiers, vehicle categories, or special characters.