## 4 Experiments
### Comparative Analysis with Existing Methods  
To demonstrate the effectiveness of our approach, we conducted a series of experiments on the Raw, Aug1, and Aug2 datasets, employing both the most current and classic methods. To ensure fairness in our comparisons, our selected methods encompass various aspects, including visual Transformers like ViTSTR [40] and MGP-tiny-char [21]. Additionally, classic methods for feature extraction using CNN, such as CRNN [38] and TRBA [1], were also included. Additionally, we incorporated the CDistNet method, which combines CNN and Transformer for joint feature extraction [52]. To serve as the baseline for this article, we introduced the PARSeq method [53], involving the joint learning of internal language models. When training exclusively on the original dataset, the method described in Table 2 failed to converge. Consequently, we opted not to present the data in Table 2. Therefore, data augmentation is considered a crucial prerequisite for text recognition in low-resource scenarios. As depicted in Table 2, our approach achieved outstanding results by training solely on the Raw + Aug1 dataset, achieving an accuracy of 94.1%, significantly outperforming other methods. Compared to the baseline model PARSeq, our method demonstrated a 48.4% accuracy improvement on the Raw+Aug1 dataset and an 11.7% improvement on the Raw + Aug1 + Aug2 dataset. Overall, irrespective of the training set conditions, our method outperforms the baseline model by 14.1% in terms of accuracy. The accuracy of our approach on the Raw + Aug1 + Aug2 dataset was lower than that on the Raw + Aug1 dataset. This is attributed to our method's ability to obtain sufficiently stable features with Raw + Aug1, whereas Aug2, an additional augmentation on Aug1, introduced more unstable features, adversely affecting the feature extraction process. Therefore, achieving excellent results can be accomplished using the Raw + Aug1 dataset alone. This also indirectly substantiates that our approach can, to some extent, mitigate reliance on training data. Although our method's accuracy on Raw + Aug1 + Aug2 was lower than the model trained only on Raw + Aug1, it still outperformed other models trained on Raw + Aug1 + Aug2. \begin{table}
\begin{tabular}{l c c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Test Accuracy (\%) / Train Dataset**} & \multirow{2}{*}{**Params (M)**} \\ \cline{2-2} \cline{4-4}  & **Raw + A1** & & **Raw + A1 + A2** \\ \hline CRNN & 78.7 & 80.2 & 8.3 \\ TRBA & 87.2 & 90.3 & 49.8 \\ ViTSTR & - & 73.1 & 21.7 \\ PARSeq & 45.7 & 80.0 & 23.8 \\ CDistNet & 64.7 & 82.5 & 65.5 \\ MGP-tiny-char & 27.2 & 33.6 & 5.4 \\ \hline CEM-tiny(ours) & 91.7 & 90.4 & 24.1 \\ CEM-base(ours) & 94.1 & 91.7 & 26.2 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison experiment with other methods. CEM-tiny is a model in the CNN branch that employs only three convolutional operations. At the same time, the CEM-base is a model in the CNN branch utilizing the architecture of ResNet17, as detailed in Section 4.5. A1 and A2 represent the datasets Aug1 and Aug2, respectively. Figure 5: Character set of the Uyghur. We also observe that the training results of other models on Raw + Aug1 + Aug2 are superior to their training results on the Raw + Aug1 dataset. This is because other methods have higher requirements for data volume. When the training data are limited, they cannot obtain sufficient information, further demonstrating the effectiveness of the method proposed in this paper. In Table 2, significant differences among the data are evident, particularly with ViTSTR and MGP exhibiting poorer performance. This discrepancy can be attributed to both methods utilizing ViT's pre-training data during training, leading to subpar results in Uyghur scene text recognition due to language variations. The use of a pure Transformer architecture, coupled with the relatively small size of our dataset, contributes to this experimental situation.