## 6 Discussion  
In this paper, we conducted license plate recognition experiments using the EAND-LPRM model on publicly available dataset as well as proprietary dataset generated by our team. Based on the encoder-decoder architecture, the EAND-LPRM model achieved outstanding recognition accuracy of 94% under various complex conditions, significantly improving the accuracy of license plate recognition technology in diverse and complex scenarios. The encoder component of the EAND-LPRM model utilizes a convolutional neural network to extract deep features from the license plate information, enabling automatic sequence alignment. This approach aligns with existing methods that have leveraged CNN for similar tasks. For instance, [PERSON] et al. [26] proposed a CNN-based method for automatic vehicle license plate recognition under various conditions. [PERSON] et al. [27] employed the YOLOv5-based Plate Detection and License Plate Recognition (YOLOv5-PDLPR) model, which integrates a multi-head attention mechanism for character recognition and a global feature extractor to improve feature completeness. They also utilized a parallel decoder architecture to enhance inference efficiency. While their method showed improved accuracy and robustness in complex scenes, our EAND-LPRM model's use of a self-attention \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Best** & **Avg** & **Test** & \multirow{2}{*}{**Loss**} \\  & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & \\ \hline CRNN & 81 & 72 & 80 & 0.231 \\ ResNet18 & 88 & 83 & 89 & 0.130 \\ ResNet50 & 88 & 83 & 91 & 0.160 \\ EAND-LPRM & 94 & 89 & 94 & 0.111 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results on the self-Made dataset CMPD. Figure 4: Model evaluation scatter plot. mechanism within the decoder similarly focuses on critical features but also emphasizes the relative positioning of characters, potentially offering enhanced recognition accuracy in dynamic and adverse conditions. Our findings not only align with the innovations in the current literature but also build upon them by introducing unique elements like the self-attention mechanism, which further refines the model's capability to handle complex and diverse license plate recognition scenarios. However, our method introduces a self-attention mechanism within the decoder, which enhances the model's ability to focus on critical features and capture the relative positions of license plate characters more effectively. In the encoder component, a convolutional neural network was utilized to extract deep features from the license plate information, enabling automatic sequence alignment. The fusion of convolutional neural networks with residual networks effectively mitigated challenges related to gradients. Furthermore, the decoder incorporated a self-attention mechanism, proficiently capturing and memorizing the relative positions of license plate characters. This enhancement bolstered the model's understanding and recognition capability of license plate characters, resulting in more accurate character predictions and higher recognition accuracy. Therefore, the model demonstrated superior adaptability to the various complexities of license plate recognition tasks, successfully capturing the intricate features of license plate images even under different and complex conditions. In our study, to support our claim of the superior performance of the EAND-LPRM model, we conducted detailed statistical analyses. By comparing with baseline models (such as CRNN, ResNet18, ResNet50), we used Independent Samples \(t\)-test or Analysis of Variance tests to evaluate whether the differences in performance across various evaluation metrics were statistically significant. The results of the statistical analyses indicate that the EAND-LPRM model significantly outperforms other methods across all four evaluation metrics.