## 2 Related Work  
As a classical text recognition network, CRNNs were introduced as early as 2015, while new network structures have emerged in recent years. Recognition models based on publicly available text recognition datasets have also made great progress up to now. Currently applied to other public languages, such as English and Chinese characters, many mature text recognition models and algorithms have been created based on these languages due to their huge number of speakers and researchers, which has profound implications for Uyghur recognition. Both [PERSON] et al. [11] and [PERSON] et al. [12] adopted the idea of phased text recognition using different network structures as replacements to train trade-offs for optimal models. [PERSON] et al. [13] implemented a unified combination of correction network and recognition network training for irregular images. [PERSON] et al. [14] changed the loss function to the ACE loss function instead of CTC, which also obtained good results. [PERSON] et al. [15] studied Chinese character recognition with respect to the characteristics of the Chinese language. In addition, in 2016, [PERSON] et al. [9] fully exploited the use of a recursive convolutional layer (RCL) to improve the feature extraction network, and later [PERSON] et al. [3] improved the recurrent convolutional layer based on this by adding gating units to construct a GRCNN. Nowadays, most of the Recurrent Neural Networks that are part of the recognition network still use the classical BiLSTM network; [PERSON] et al. [10] proposed to remove the Recurrent Neural Network part directly to solve this problem and performed the final prediction directly with visual feature sequences, still obtaining terrific results. In the final sequence prediction stage, [PERSON] et al. [16] developed the use of an attention mechanism to replace the use of CTC in 2017 to predict the last value, i.e., the RARE model. In the same year, [PERSON] et al. [17] introduced the method of text recognition by combining ResNet with an attention mechanism. In 2023, [PERSON] et al. [18] proposed the use of real-time scene text detection with differentiable binarization (DBNet) [19], a combination of a text orientation classifier and the Retinex algorithm for image enhancement, to improve the accuracy of text recognition in complex scene images. In a similar Uyghur sticky script, [PERSON] et al. [20] used different text recognition structures (CRNN with ResNet) as a feature extraction network versus VGG as a feature extraction network in their study of the Urdu language and confirmed that CRNN text recognition structures using residual links have better recognition accuracy. In 2023, [PERSON] et al. [21] used a Convolutional Neural Network for classification and recognition of Urdu language (and its variants) datasets and developed handwritten Urdu language digit and Pakistani currency digit datasets. [PERSON] et al. [22] presented a review of Arabic recognition, where they presented the entire process of optical character recognition (OCR) for Arabic, including some of the most advanced techniques, and provided future research directions for Arabic recognition. After this, [PERSON] et al. [23] provided the latest deep learning techniques for OCR technology and correction after OCR for Arabic handwriting, developing a practical space for future trends in Arabic OCR applications. There have been some excellent deep learning algorithms proposed in the research of Uyghur text recognition. In 2017, [PERSON] [24] of Xi'an University of Electronic Science and Technology in China conducted research on the application of key technologies for printed Uyghur text recognition, obtaining several contiguous segments and single standing characters by the morphological expansion method for line slicing. After this, the contiguous segments were sliced into characters by the vertical projection method.