## 3 Methodology
### The Proposed Model
\begin{table}
\begin{tabular}{c c c c} \hline
**Level** & **Layer** & **Params** & **Output** \\ \hline Input & - & - & \(512\times 512\) \\ \hline Conv\_1\_1 & ConvBNLayer & \(3\times 3\), in = \(3\), stride = \(2\) & \(256\times 256\) \\ \hline Conv\_1\_2 & ConvBNLayer & \(3\times 3\), in = \(32\), stride = \(1\) & \(256\times 256\) \\ \hline Conv\_1\_3 & ConvBNLayer & \(3\times 3\), in = \(32\), stride = \(1\) & \(256\times 256\) \\ \hline MaxPool1 & MaxPool2d & \(3\times 3\), stride = \(2\), padding = \(1\) & \(128\times 128\) \\ \hline Block1 & ResBlock & \(\left[\begin{array}{c}3\times 3,64\\ 3\times 3,64\end{array}\right]\times 3\) & \(128\times 128\) \\ \hline Block2 & ResBlock & \(\left[\begin{array}{c}3\times 3,128\\ 3\times 3,128\end{array}\right]\times 4\) & \(64\times 64\) \\ \hline Block3 & ResBlock & \(\left[\begin{array}{c}3\times 3,256\\ 3\times 3,256\end{array}\right]\times 6\) & \(32\times 32\) \\ \hline Block4 & ResBlock & \(\left[\begin{array}{c}3\times 3,512\\ 3\times 3,512\end{array}\right]\times 3\) & \(16\times 16\) \\ \hline DeConv1 & DeConvBNLayer & \(4\times 4\), in = \(512\), stride = \(2\) & \(32\times 32\) \\ \hline DeConv2 & ConvBNLayer & \(3\times 3\), in = \(384\), stride = \(1\) & \(32\times 32\) \\ \hline DeConv3 & DeConvBNLayer & \(4\times 4\), in = \(128\), stride = \(2\) & \(64\times 64\) \\ \hline DeConv3 & ConvBNLayer & \(3\times 3\), in = \(256\), stride = \(1\) & \(64\times 64\) \\ \hline DeConvBNLayer & DeConvBNLayer & \(4\times 4\), in = \(128\), stride = \(2\) & \(128\times 128\) \\ \hline DeConv4 & ConvBNLayer & \(3\times 3\), in = \(192\), stride = \(1\) & \(128\times 128\) \\ \hline \multirow{3}{*}{UpConvBlock1} & ConvBNLayer & \(3\times 3\), in = \(128\), stride = \(1\) & \(128\times 128\) \\ \cline{2-4}  & ConvBNLayer & \(3\times 3\), in = \(128\), stride = \(1\) & \(128\times 128\) \\ \hline \multirow{3}{*}{UpConvBlock1} & Upsample & \(\text{size}=(256,256)\) & \(256\times 256\) \\ \cline{2-4}  & Conv2d & \(3\times 3\), in = \(32\), stride = \(1\) & \(256\times 256\) \\ \hline \multirow{3}{*}{UpConvBlock2} & Upsample & \(\text{size}=(512,512)\) & \(512\times 512\) \\ \cline{2-4}  & Conv2d & \(3\times 3\), in = \(32\), stride = \(1\) & \(512\times 512\) \\ \hline DownConv1 & Conv2d & \(3\times 3\), in = \(128\), stride = \(2\) & \(64\times 64\) \\ \hline DownConv2 & Conv2d & \(1\times 1\), in = \(384\), stride = \(1\) & \(64\times 64\) \\ \hline DownConv3 & Conv2d & \(3\times 3\), in = \(256\), stride = \(2\) & \(32\times 32\) \\ \hline DownConv4 & Conv2d & \(1\times 1\), in = \(640\), stride = \(1\) & \(32\times 32\) \\ \hline \multirow{3}{*}{DetConv} & ConvBNLayer & \(3\times 3\), in = \(512\), stride = \(1\) & \(32\times 32\) \\ \cline{2-4}  & ConvBNLayer & \(3\times 3\), in = \(128\), stride = \(1\) & \(32\times 32\) \\ \hline \multirow{3}{*}{ClsConv} & ConvBNLayer & \(3\times 3\), in = \(512\), stride = \(1\) & \(32\times 32\) \\ \cline{2-4}  & ConvBNLayer & \(3\times 3\), in = \(512\), stride = \(1\) & \(32\times 32\) \\ \hline ResHead & Conv2d & \(1\times 1\), in = \(128\), stride = \(1\) & \(512\times 512\) \\ \hline \multirow{3}{*}{DetHead} & ConvBNLayer & \(1\times 1\), in = \(64\), stride = \(1\) & \(32\times 32\) \\ \cline{2-4}  & ConvBNLayer & \(1\times 1\), in = \(64\), stride = \(1\) & \(32\times 32\) \\ \hline \multirow{3}{*}{RecHead} & ConvBNLayer & \(1\times 1\), in = \(512\), stride = \(1\) & \(32\times 32\) \\ \cline{2-4}  & fc & in = \(1024\) & out = \(745\) \\ \cline{1-1} \cline{2-4}  & softmax & in = \(745\) & \(32\times 32\) \\ \hline \end{tabular}
\end{table} 
Table 1: Detailed parameters of the proposed network. the input image \(\mathbf{x}\in{}^{3\times H\times W}\) is passed through multiple residual blocks in the backbone to obtain intermediate representations \(\mathcal{F}_{\text{backbone}}\), which capture both the local textures and global structures of the characters. These features contain essential visual cues such as shape, contour, and texture, which support the subsequent restoration task.