## 4 Experiments
### Ablation Experiments
Without the dynamic fusion module, directly adding the features of the two branches somewhat diminishes their respective strengths. Combining the experimental results from the 1 st, 5 th, and 9 th rows, it is evident that the CNN branch module plays a vital role in the collaborative coding method for Uyghur text recognition in low-resource scenarios. When using a 17-layer ResNet architecture, the accuracy improves by 2.4% compared to the model using only three convolutional modules. However, when using a 33-layer ResNet architecture, although the model surpasses the one using only three convolutional operations, the accuracy is lower than that of the 17-layer ResNet architecture. This may be due to the excessive complexity of the CNN branch structure, causing the model parameters to be too many for the limited dataset, leading to incomplete model convergence. Data from the experiment's 1 st, 5 th, and final rows indicate that the model performs poorly when the CNN branch is not utilized. This further supports that the CNN-Transformer dual-branch cooperative encoding can obtain more comprehensive feature representations and encoding. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Filter**} & \multirow{2}{*}{**CNN**} & \multirow{2}{*}{**CBAM**} & \multirow{2}{*}{**DF**} & \multicolumn{2}{c}{**Test Accuracy (\%) / Train Dataset**} \\ \cline{5-5}  & & & & & **Raw + A1** \\ \hline ✓ & 3-Con & ✓ & ✓ & 91.7 \\ ✗ & 3-Con & ✓ & ✓ & 83.5 \\ ✓ & 3-Con & ✗ & ✓ & 91.5 \\ ✓ & 3-Con & ✓ & ✗ & 91.6 \\ ✓ & ResNet17 & ✓ & ✓ & 94.1 \\ ✗ & ResNet17 & ✓ & ✓ & 93.5 \\ ✓ & ResNet17 & ✗ & ✓ & 93.2 \\ ✓ & ResNet17 & ✓ & ✗ & 92.3 \\ ✓ & ResNet33 & ✓ & ✓ & 93.4 \\ ✗ & ResNet33 & ✓ & ✓ & 92.6 \\ ✓ & ✗ & ✓ & ✓ & 79.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiments of the Filter module, CNN Feature Extraction Branch, and DF module. In the Filter module, the symbol ✗ indicates that the Filter module is not used, and the symbol ✓ indicates that the Transformer branch and the CNN branch share a common Filter module. For 3-Con, it denotes three consecutive convolution operations. In the CBAM and dynamic fusion modules, ✓ and ✗, respectively, represent the utilization and non-utilization of the module. To verify the impact of different encoding layer numbers in the Transformer branch on the model in the collaborative coding method proposed in this paper for Uyghur text recognition in low-resource scenarios, experiments were designed with Transformer branch encoding layer numbers of 6, 8, and 12. Meanwhile, a lightweight model with three convolutional operations was adopted in the CNN branch, as shown in Table 4. All experiments in Table 4 used the Filter module, CBAM, and DF methods. Table 4 shows that the model performs best when the Transformer branch adopts a 6-layer ViT layer. This is because in situations with limited training data, an excessive number of encoder layers can lead to an increase in parameters, adversely affecting the convergence of the model. Therefore, in this article, the collaborative coding method chose a 6-layer ViT layer for the Transformer branch. This research result emphasizes the importance of selecting an appropriate number of encoder layers for model performance in text recognition tasks in low-resource contexts. Our experimental results provide valuable references for designing more effective text recognition models tailored to low-resource contexts.