# Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
## 2 Multi-Scale-SE Recognition Methods
### Multi-Scale Fusion Feature Extraction  
In the layer of convolutional neural network, take 2D image data as an example, suppose the input feature map has height H, width W, and the number of channels Cin. After the convolution operation, the generated feature map will have a new height H', width W, and a new number of channels Cout [25]. Since a single convolutional kernel can only capture the features of a specific pattern, in order to enhance the expressive power of the network and to extract more diversified features, it is common for multiple convolutional kernels to be introduced. Multi-scale information fusion has long been a focal point in the field of image processing and computer vision [26].  
For the input feature map \(X\), different sizes of convolution kernels (e.g., 3 \(\times\) 3 and 5 \(\times\) 5) can be used to extract multi-scale information. These convolutional kernels are able to capture different patterns of the input features and combine them into richer representations, helping to improve the network's adaptability to complex tasks. In this way, multi-scale features are fused into the feature representation of the network, thus enhancing the overall performance.  
The process is as follows: a convolution operation is performed on the input feature map \(X\) using a convolution kernel of size \(K_{1}\) to obtain the feature map:  
\[Y_{1}=ConvK_{1}(X)+b_{1} \tag{4}\]  
A convolution operation is performed on the input feature map \(X\) using a convolution kernel of size \(K_{2}\) to obtain the feature map:  
\[Y_{2}=ConvK_{2}(X)+b_{2} \tag{5}\]  
Next, the feature maps are dimensionally aligned, usually through an upsampling or downsampling operation, to bring the output feature maps to a uniform size:  
\[\begin{array}{l}\gamma_{1}^{\text{aligned}}&=Upsample(Y_{1})\\ Y_{2}^{\text{aligned}}&=Upsample(Y_{2})\end{array} \tag{6}\]  
After completing the dimensional alignment, the two feature maps are spliced in the channel dimension to obtain the fused feature map \(Y\):  
\[Y=\text{Concat}(Y_{1}^{\text{aligned}},Y_{2}^{\text{aligned}}) \tag{7}\]  
Through the above multi-scale convolution and feature fusion, the network can more fully acquire information at different scales and improve the representation of features. In the handwritten Chinese character recognition task, the traditional 3 \(\times\) 3 convolutional kernel design has been proven to have efficient feature extraction capability but still has some limitations when facing the complex, multi-scale visual information extraction of handwritten Chinese character images. In particular, simple characters occupy a large proportion of the writing grade test, but the feature scales of these simple characters differ significantly from those of complex-shaped characters. This difference in feature scales poses a special challenge to the design of the convolution kernel of the model: on the one hand, a larger convolution kernel can effectively increase the receptive field to more adequately capture the structural features and shape differences in complex characters; on the other hand, a smaller convolution kernel reduces the amount of computation in extracting the detailed features of the simple characters, which helps to avoid the problems of over-computation of the simple characters as well as the under-exploitation of the features of the complex characters.  
To address this problem, this paper introduces a combination of parallel 3 \(\times\) 3 and 5 \(\times\) 5 convolutional kernels in the bottleneck layer to enhance the feature extraction capability. The structure enables the model to process multi-scale features in parallel at the same level, capturing local details through 3 \(\times\) 3 convolutional kernels while using 5 \(\times\) 5 convolutional kernels to encompass a larger sensory field, thus acquiring rich contextual information. Figure 3 shows a diagram of multi-scale feature extraction.