## 1 Introduction  
The Uyghur language is difficult to recognize due to the sticky nature of its words, the variability of its characters, and the right-to-left nature of its writing. Specifically, the following four key problems need to be addressed: 1. Uyghur datasets are scarce. Uyghur scripts were recognized late and Uyghur script technology for printed text has only been studied in recent years. There is no standard Uyghur dataset in the world, so we need to build our own Uyghur dataset. 2. Uyghur characters have a multivariate nature, leading to a difficult recognition process. Uyghur characters are variable; the same character in different positions of the word can have different forms and a character showing different forms may be very similar to another character in another position. For example, the character \"_B_\" in the word \"_y_y_y_y_\" will become \"_y_y_\" before the character \"_s_\" (Uyghur is written from right to left), and the first half of the two characters that are stuck together is quite similar to the character \"_s_\", which may lead to unsatisfactory final results. 3. Uyghur has strong adhesion which makes the recognition process infinitely difficult. At the same time, it also causes all the letters in the same word to basically stick together without gaps, so that some of the Uyghur characters are extremely similar, such as the characters \"_y_\" and \"_y_\". This makes the recognition of Uyghur characters much more difficult than those of English or other languages. When using \"connectionist temporal classification\" (CTC) [1] for prediction, the same frame may contain information of two characters that are stuck together, and the adjacent pages before and after the frame may appear with the same condition, which may contribute to the missed predictions, over-predictions, or wrong predictions in CTC, especially in the prediction of long words, such as the word \" _et al._ \" being predicted as \" _et al._ \". The combination of the character \"_s_\" and the character \"_o_\", \"_\", is predicted to be the character \"_o_\" in the dense part of the middle of the word. We also found that the overall effect of decoding prediction using CTC was not excellent during experiments. 4. There are a vast number of Uyghur words. There are a lot of common words in Uyghur and Uyghur sentences are even more diverse after combination. Therefore, to accurately identify Uyghur words in a line of text requires training on a large-scale dataset. To address the above challenges, the research aim of this paper is as follows. First, a comprehensive and usable Uyghur text dataset is produced by taking line-level printed Uyghur text as the research object. After that, we propose a three-stage model structure for Uyghur text recognition for the Uyghur dataset by combining models that have been applied and not used for Uyghur text recognition before for training. For the variability of Uyghur characters, we use more complex and excellent feature extraction networks to extract more detailed features of Uyghur text, such as \"Very Deep Convolutional Networks\" (VGG) [2], \"Convolutional Neural Networks With Gated Recurrent Connections\" (GRCNN) [3], \"Deep Residual Network\" (ResNet) [4] and \"ConvNet for the 2020s\" (ConvNeXt) [5]. These methods are used to extract visual features of Uyghur images. For the textual characteristics of Uyghur with strong adhesion, we use the attention mechanism (Attn) [6] instead of CTC for prediction. In addition, we also remove the \"bi-directional long short-term memory\"(BiLSTM) [7] layer to reduce the overall parameters of the network and improve the performance of the network. Eventually, the trade-off was evaluated between the model parameters and the end accuracy to ultimately select the most suitable language model for Uyghur language recognition.