## 2 Related Work  
In recent years, large-scale pre-trained language models have demonstrated powerful language perception capabilities across various computer vision tasks [27; 28; 29; 30; 31; 32]. Scholars have also applied these models to the field of scene text recognition [33; 34; 35; 17]. We categorize scene text recognition methods based on their reliance on language models, distinguishing between those with and without such dependencies [19]. Additionally, we classify the usage of language models into two types: Scene Text Recognition Methods Guided by External Language Models and Scene Text Recognition Methods Employing Joint Learning of Internal Language Models. Scene Text Recognition Methods Without Language Models: These methods primarily rely on visual information for text recognition, disregarding language relationships between characters and typically predicting characters independently. One classical method in this category is based on Connectionist Temporal Classification (CTC) [36]. The Rosetta [37] adopts a two-stage text recognition framework utilizing ResNet and CTC loss. This model accommodates inputs of variable sizes and predicts texts of arbitrary lengths. However, the reliance on monotonic alignment and conditional independence assumptions in CTC-based methods [37; 38; 39] results in the neglect of semantic information between characters in the text. With the widespread application of Transformers in the field of computer vision, some approaches [21; 40; 41] have started to leverage Vision Transformers (ViTs) [22] for scene text recognition. Among them, ViTSTR [40] introduces ViT methods to achieve a straightforward scene text recognition model architecture. SVTR [41] builds upon ViT by incorporating local and global fusion blocks dedicated to extracting stroke-like features and capturing dependencies between characters. Additionally, it integrates a multi-scale backbone network, enabling a multi-granular feature description. Similarly, MGP-STR [21] directly employs ViT for feature extraction but introduces multi-granular character predictions. These methods utilize ViT encoders for feature extraction and then map these features to category labels through fully connected layers, achieving a certain degree of success. However, their exclusive reliance on visual features for prediction results in poorer performance on low-quality images. Given the relatively small size of the Uyghur language real-world scene dataset, methods relying solely on visual information exhibit relatively weaker performance on small datasets. This paper incorporates co-encoding in the encoding phase to address this issue and simultaneously adopts internal language models for joint learning. Scene Text Recognition Methods Guided by External Language Models: These methods utilize externally trained language models that do not participate in the overall model's backpropagation process. Some external pre-trained methods pre-train all branches of the model or only the language branch [42; 43]. ABiNet [42] has separately pre-trained the visual branch and the language branch, employing an iterative correction method to alleviate errors in the visual branch predictions. TrOCR [43] accomplishes image understanding and word-level text generation through pre-trained Image Transformer and Text Transformer models. Recent trends lean toward utilizing large-scale multi-modal models such as CLIP [18], ALIGN [44], Florence [45], and ChatGPT4 for external guidance [33; 17; 35]. CLIP4 STR [35] leverages the pre-trained CLIP model to guide the training of its visual model. CLIP-OCR [33] introduces a symmetric language feature distillation framework designed to leverage both visual and language knowledge within CLIP to the fullest extent. Despite their outstanding results, training external language models often requires extensive data, and these models are typically designed for resource-rich languages.