## 3 Modeling  
The proposed EAND-LPRM method consists of three components: an encoding feature extraction module, decoding sequence transformation module, and sequence label prediction module. These components are connected in an end-to-end manner to address deformations and disturbances in license plate images while achieving optimal processing speed. The overall architecture is illustrated in Figure 1.  
The encoding feature extraction module employs a convolutional neural network comprising 29 independent convolutional layers and residual blocks. The initial convolutional layers of the model are utilized for extracting low-level features from the input license plate images. By gradually adjusting the output channels, we constructed a deeper network architecture aimed at capturing deeper-level information of the license plate characters. To mitigate gradient explosion issues, residual connections were introduced into the encoder. This not only alleviates problems related to gradients but also enhances the depth of the network, enabling it to capture complex deep featuresimages. When dealing with complex scenes and license plates, the model learns additional features, thereby improving recognition performance. It is noteworthy that our output channels first pass through 64 layers, and then, through 32 layers, with the specific channel transformation module illustrated in Figure 2. This progressive reduction in the number of channels allows the model to compress information during the feature extraction stage, effectively preserving the important features of the input license plate images. By increasing the number of channels in the later layers, we can flexibly integrate both low-level and high-level features of the license plate characters. This approach enables the model to capture features of different depths, thereby identifying various complex details of license plates. It enhances its flexibility in diverse environments and strengthens its ability to recognize license plate images across multiple complex scenarios.  
In the decoding sequence transformation module, we integrate the self-attention mechanism into the decoder architecture to effectively model the sequence dependencies between license plate characters. This helps the model to focus more on the important feature information at each position within the license plate data, enabling it to better understand the contextual relationships between characters, thereby enhancing its ability to capture key information and improve character recognition accuracy. The self-attention  
Figure 1: Structure diagram of the EAND-LPRM model.  
mechanism directly replaces global convolution, making the model more lightweight while also increasing its efficiency and speed. This mechanism further enhances the model's adaptability to various complex environments, particularly demonstrating exceptional capabilities when handling low-quality license plate images.  
In the sequence label prediction module, the connectionist temporal classification (CTC) method is employed to process the decoder's output results, mapping input sequences to output sequences without the need for explicit alignment between input and output sequences. This makes the model more flexible, suitable for input and output sequences of variable lengths, effectively addressing the challenge of varying numbers of license plate characters due to the diversity of license plate types in the dataset. This method is unaffected by duplicate characters and can accurately identify license plates with different character lengths. Therefore, this approach significantly enhances the model's generalization ability, ensuring precise recognition across various license plate formats and variations.