## 1 Introduction  
Scene text recognition (STR) [1; 2; 3; 4; 5] is a computer vision technology designed to extract and convert text from natural images into a computer-understandable character sequence. This technology finds widespread applications in various fields [6; 7] including but not limited to autonomous driving, intelligent surveillance, image search, and document recognition. Unlike traditional Optical Character Recognition (OCR) [8; 9; 10; 11; 12], scene text recognition needs to address text diversity in natural scenes and tackle challenges such as complex backgrounds, scale variations, lighting differences, and variable fonts. Traditional scene text recognition typically involves identifying text through the detection of individual characters [13; 14; 15]. In contrast, contemporary methods based on deep learning transform the recognition problem into sequence prediction [16], thereby bypassing the intricate feature extraction and classification processes in traditional approaches. Currently, deep learning-based scene text recognition methods have achieved certain achievements. CLIP4 STR [17] has expelled in scene text recognition by introducingthe CLIP [18] large language pre-trained model. TrOCR [19] adopts pre-trained image Transformer and text Transformer models for image understanding and word-level text generation. CPDD [20] proposes a context-aware parallel decoder, while MGP-STR [21] predicts characters at different granularities. However, most research currently focuses on languages with abundant resources such as English and Chinese. Yet, there is relatively little research on low-resource languages like Uyghur, Mongolian, Kazakh, and Kyrgyz, which lack real-world scene text recognition data. Mainstream methods primarily rely on Transformer-based frameworks, which are often characterized by more parameters. While this grants the models enhanced representational capabilities, it also intensifies their reliance on substantial amounts of annotated data. As a result, when dealing with limited resources, models based on the Transformer architecture encounter challenges in achieving effective convergence compared to methods based on CNN, leading to relatively poorer performance [22]. Currently, research on Uyghur language text recognition primarily focuses on printed and handwritten text recognition [23; 24; 25; 26]. However, there is limited research on scene text recognition for the Uyghur language. Uyghur language characters possess distinctive features, such as varying forms at different positions within words, with some characters having three or more forms. The Uyghur language is written from right to left, and characters can form ligatures during writing, meaning they can connect and transform, creating a continuous character sequence that increases the difficulty of recognizing individual characters. Additionally, the differences between some characters are extremely subtle. All these characteristics pose certain challenges to existing scene text recognition methods. To address the scarcity of Uyghur scene text data, we have constructed a real-world Uyghur language dataset named the Raw dataset. Using this dataset, we applied data augmentation techniques to generate two extended scene datasets, Aug1 and Aug2. The Raw dataset comprises 9082 street scene images, including road signs, billboards, book titles, and other common street text images. Of these, 7267 images were used for training, and 1815 were reserved for testing. The Aug1 dataset was created by augmenting the 7267 training images from the Raw dataset, resulting in 254,345 images. The Aug2 dataset, generated through a second round of data augmentation on the Aug1 dataset, consists of 3,052,140 scene images. Data augmentation techniques can help somewhat alleviate the problem of insufficient training data for low-resource languages. In response to the characteristics of Uyghur language characters, this paper adopts a collaborative encoding method using both CNN and Transformer.