## 5 Results and Discussion
### Classification Experiments
Employing the same three models on a dataset comprising a mixture of printed and handwritten Uyghur texts, we achieved accuracy rates of 69.03%, 86.19%, and 89.32%, respectively. However, our most advanced model, ConvNeXt+BiLSTM+Attn, demonstrated a remarkable accuracy rate of 90.21%. This achievement underscores the superiority of our approach. This accomplishment not only serves as a definitive benchmark but also underscores the distinctive value and effectiveness of our method in handling Uyghur text recognition. In the trade-off between accuracy and time, we find that the overall trend in test time is similar to that of accuracy and the number of parameters, with the difference in the test time using ResNet as a feature extraction network is better than that of the CRNN network, even though the number of parameters in Resnet is higher. The best points overall are P4 (VGG + BiLSTM + Attn) and P12 (ResNet + BiLSTM + Attn), with accuracies of 84.70% and 89.32%, respectively, achieving a relatively high accuracy in a shorter test time. The optimal point of accuracy is P16 (ConvNeXt + BiLSTM + Attn) with an accuracy of 90.21%, as shown in Figure 21.  
Figure 20: Accuracy—parametric chart. Corresponding to CTC is the use of the attention mechanism for prediction; as shown in Figure 22, we can clearly observe regardless of the feature extraction network and regardless of whether BiLSTM is used for sequence model modeling, excellent recognition results can be achieved, all reaching more than 80%. In particular, the three best performing points are P8, P12 and P16. The P8 network structure can achieve a better accuracy with a lower number of parameters using RCNN and BiLSTM with Attn. The second best is the P12 network structure, combining ResNet and BiLSTM with Attn, which has an increased number of network parameters and a slightly improved recognition accuracy compared to the P12 network. Finally, if the P16 network structure combining ConvNeXt and BiLSTM with Attn is used without taking into account the number of training parameters of the network, the optimal accuracy of 90.21% can be achieved. Thus, why does the use of the attention mechanism improve the accuracy of Uyghur recognition so much? After our analysis, it was determined that the memory of the network structure combining BiLSTM with CTC is not high and can only alleviate part of the long- and short-term memory problems, while the use of the attention mechanism enabled focusing on the global situation and can better deal with the semantic information of the text. It has an important role in the recognition process of Uyghur because Uyghur words are generally long and stick together, which requires a high level of training of semantic information. In addition, during training, we found that the network structure using the attention mechanism tends to converge much faster than the network structure using CTC prediction, so we believe that the use of the attention mechanism is more effective in Uyghur recognition. In terms of model testing time, the inference time of models using the attention mechanism is generally longer than that of models using CTC. However, this is accompanied by an increase in accuracy, as shown in Figure 23.  
The effect of the network feature extraction layer on the overall recognition network is shown in Figure 24. In terms of parameters, the RCNN has the least number of parameters, followed by VGG, then ResNet and finally ConvNeXt. Overall, the accuracy of recognition tends to increase as the parameters of the network increase, especially in the model structure using CTC. In the case of the model structure using the attention mechanism, the trend is slower because the recognition accuracy has reached a high level; however, in general, the recognition accuracy increases with the complexity of the feature extraction network. Figure 21: Accuracy—time chart. In the comparison of model testing times using different feature extraction networks, the VGG network performs better at low model testing times.