## 4 Experiments
### Ablation Experiments  
We conducted a series of ablation experiments to validate the effectiveness of the proposed collaborative encoding method for text recognition in low-resource Uyghur language scenarios. Firstly, we performed detailed ablation experiments on the filter, CNN feature extraction branches, and dynamic fusion modules. The main experimental groups include the following:  
1. Using the filter module before the dual-branch feature extraction module and not using the filter module. 2. Utilizing three consecutive convolutional layers, ResNet17, and ResNet33 for the CNN branch. 3. Applying the CBAM module and not applying the CBAM module in the CNN branch. 4. Employing the dynamic fusion module and not employing the dynamic fusion module. 5. Investigating the impact of different encoding layers in the Transformer branch on model performance. To present the experimental results more clearly, we organized experiments (1) to (4) into groups in Table 3, providing detailed descriptions of the specific settings in the table. In Table 3, the Transformer branch in the experiments uniformly adopted a six-layer ViT encoder. The results of the experiment group (5) are presented in detail in Table 4.  
Observing the data in the 1 st and 2 nd rows of Table 3, when only three convolutional operations are employed in the CNN branch and a filtering module is incorporated before the dual-branch feature extraction module, the model achieves an 8.2% increase in accuracy compared to the model without the filtering module. This is because the CNN branch employs only three consecutive convolution operations. Without the filtering module, the CNN branch's contribution is limited, leading to excessive reliance on the encoding module on the Transformer branch and hindering model convergence. By examining the data in the 5 th and 6 th rows of Table 3, it can be observed that when using ResNet17 for feature extraction in the CNN branch, incorporating a filtering module before the dual-branch feature extraction module led to a 0.6% increase in model accuracy compared to the model without the filtering module. Based on the data in the 1 st, 2 nd, 5 th, 6 th, 9 th, and 10 th rows, it can be observed that the filter module has the greatest impact when the CNN branch employs only three convolution operations. When the CNN branch uses ResNet17, its effect is reduced, but the accuracy is still 0.6% higher than models that do not use the filter module. However, data from the 9 th and 10 th rows indicate that enhancing the CNNbranch cannot fully replace the role of the filter module. In this case, even though the CNN branch adopts the architecture of ResNet33, models using the filter module still achieve an accuracy improvement of 0.8%. This further demonstrates that the filter module can help the model better extract text features and reduce the influence of background noise. The analysis of the data in the 1 st and 3 rd rows found that the accuracy of the model improved by 0.2% when the CBAM module was incorporated into the CNN branch using a lightweight architecture. Similarly, based on the data in the 5 th and 7 th rows, it can be inferred that the accuracy of the model increased by 0.9% when the CBAM module was integrated into the CNN branch using the ResNet17 architecture. This further validates that CBAM can better assist the model in extracting key features, thereby improving the model's accuracy. The experimental data in the 1 st and 4 th rows, and the 5 th and 8 th rows, demonstrate that when the CNN branch adopts a lightweight architecture, the model's accuracy increases by 0.1% with the use of the dynamic fusion module. When the CNN branch adopts the ResNet17 architecture, the model's accuracy improves by 1.8% after employing the dynamic fusion module. This is because with the dynamic fusion module, the model can dynamically select the most suitable proportion of CNN and Transformer branches during the training process, maximizing both advantages.