# Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
## 2 Multi-Scale-SE Recognition Methods
### The Bottleneck Layer Structure of the Fusion Attention Mechanism  
In the design of convolutional neural networks (CNNs), the bottleneck layer structured as '\(1\times 1\)-\(3\times 3\)-\(1\times 1\)' with three convolutional kernels is a classic design to optimize the network efficiency and feature extraction capability, especially widely used in deep networks such as ResNet [23]. This bottleneck layer structure significantly improves the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **MSSE Model** & **Output Shape** \\ \hline Input & 96 \(\times\) 96 image & 96 \(\times\) 96 \(\times\) 1 \\ \hline Conv1 & 3 \(\times\) 3 conv & 96 \(\times\) 96 \(\times\) 64 \\ \hline Conv2 & 3 \(\times\) 3 conv & 96 \(\times\) 96 \(\times\) 64 \\ \hline AvgPool & 3 \(\times\) 3 avgpool & 48 \(\times\) 48 \(\times\) 64 \\ \hline \multirow{3}{*}{MSSEBottleneck1} & 1 \(\times\) 1 conv & \multirow{3}{*}{48 \(\times\) 48 \(\times\) 96} \\  & 3 \(\times\) 3 conv/5 \(\times\) 5 conv & 48 \(\times\) 48 \(\times\) 96 \\  & 1 \(\times\) 1 conv & \\ \hline AvgPool & 3 \(\times\) 3 avgpool & 24 \(\times\) 24 \(\times\) 96 \\ \hline \multirow{3}{*}{MSSEBottleneck2} & 1 \(\times\) 1 conv & \multirow{3}{*}{24 \(\times\) 24 \(\times\) 128} \\  & 3 \(\times\) 3 conv/5 \(\times\) 5 conv & 12 \(\times\) 12 \(\times\) 256 \\  & 1 \(\times\) 1 conv & \\ \hline AvgPool & 3 \(\times\) 3 avgpool & 6 \(\times\) 6 \(\times\) 256 \\ \hline \multirow{3}{*}{MSSEBottleneck4} & 1 \(\times\) 1 conv & \multirow{3}{*}{6 \(\times\) 6 \(\times\) 448} \\  & 3 \(\times\) 3 conv/5 \(\times\) 5 conv & \\  & 1 \(\times\) 1 conv & \\ \hline GAP & gap, dropout & 1 \(\times\) 1 \(\times\) 448 \\ \hline Output & Softmax & 3755 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MSSE network-specific architecture design. Figure 1: MSSE model. performance of the model in computer vision tasks by reducing the computational effort while maintaining the integrity of the features through the 'compression-transformation-recovery' strategy [24]. Specifically, the bottleneck layer structure consists of three convolutional kernels: first, the first 1 \(\times\) 1 convolutional kernel is used to downscale the feature map, reducing the number of channels to significantly reduce the amount of computation and parameters, to achieve the effect of feature compression while retaining the key information. Next, the middle 3 \(\times\) 3 convolutional kernel extracts local features in lower dimensions, which is less computationally expensive but can effectively capture spatial information. Finally, the second 1 \(\times\) 1 convolutional kernel is used to recover the number of channels of the feature map to ensure that the feature dimensions output from the bottleneck layer match the input from the subsequent network layer. In the handwritten Chinese character recognition task, the complex structure and subtle differences in Chinese characters require the model to have accurate multi-level feature extraction capability. The design of the bottleneck layer helps enhance the feature expression ability of the model, where the 3 \(\times\) 3 convolution is good at capturing details and local features, while the 1 \(\times\) 1 convolution kernel effectively reduces the computational burden of the network through the process of dimensionality reduction and dimensionality reduction, which improves the training efficiency and inference speed. In addition, the bottleneck layer reduces feature redundancy, which not only enables the model to still achieve performance close to that of deep networks in relatively shallow networks and improves the model's ability to capture subtle features such as complex strokes and structures, but also enables deep neural networks to achieve a good balance between accuracy and computational efficiency by reducing computational cost.