## 5 Conclusions  
Research methods for text recognition in low-resource scenarios are relatively limited. Existing studies primarily focus on languages with abundant resources, and most state \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Transformer Encoder Layers**} & \multicolumn{3}{c}{**Test Accuracy (\%) / Train Dataset**} & \multirow{2}{*}{**Params (M)**} \\ \cline{2-3} \cline{5-5}  & **Raw + A1** & & **Raw + A1 + A2** \\ \hline
6 & 91.7 & 90.4 & 24.1 M \\
8 & 91.7 & 90.2 & 27.7 M \\
12 & 91.1 & 90.4 & 34.8 M \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments of the number of encoding layers in the Transformer branch. Figure 6: Examples of incorrect predictions. We employ red markings to distinguish segments where predicted characters deviate from the ground truth. of-the-art methods are based on the Transformer architecture, showing poor adaptability to low-resource datasets. This article specifically targets low-resource Uyghur text and introduces a collaborative encoding approach. The encoder comprises a filtering module, a dual-branch feature extraction module, and a dynamic fusion module. Our method achieves state-of-the-art results in Uyghur text scene recognition. Additionally, we have curated a genuine natural scene Uyghur text recognition dataset and two synthetic datasets, Aug1 and Aug2. These datasets provide robust data support for Uyghur text scene recognition research. Our method can somewhat mitigate the reliance on training data while also providing reference examples for other low-resource languages that lack real-world scene text recognition data, such as Mongolian, Kazakh, and Kyrgyz. In our future research, we will pursue our work in two main aspects. Firstly, we intend to expand our existing real-world Uyghur language dataset. Secondly, we are preparing to shift our next research objectives to other low-resource languages, such as Mongolian and Kazakh. M.X. : Methodology, Software, Writing--original draft. L.X. : Visualization, Investigation. J.Z. : Writing--review and editing. Y.L. : Supervision. W.S. : Conceptualization. All authors have read and agreed to the published version of the manuscript. The funds to support this work come from the Joint Funds of the National Natural Science Foundation of China Joint Fund Project under Grant U1911401 hosted by Wushour Silamu, and the Shenzhen Municipal Science and Technology Innovation Committee Project under Grant GJGJZD20210408092806017 hosted by Wushour Silamu. Not applicable. The data supporting this study's findings are available on request from the corresponding author or first author, upon reasonable request. The authors declare no conflicts of interest. References  
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