## 5 Results and Discussion
### Classification Experiments  
In this paper, a three-stage text recognition structure was used for Uyghur recognition experiments, developing different combinations of recognition models (4 \(\times\) 2 \(\times\) 2 = 16 kinds). The accuracy, the model testing time, the normalized edit distance, and the overall number of parameters were compared under different network structures. The comparison results are shown in Table 1.  
### Classification Experiments  
In this paper, a three-stage text recognition structure was used for Uyghur recognition experiments, developing different combinations of recognition models (4 \(\times\) 2 \(\times\) 2 = 16 kinds). The accuracy, the model testing time, the normalized edit distance, and the overall number of parameters were compared under different network structures. The comparison results are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \# & **Feat.** & **Seq.** & **Pred.** & **ACC.\%** & \
\begin{tabular}{c} **Time** \\ **ms/Image** \\ \end{tabular} &
\begin{tabular}{c} **Norm\_ED** \\ **\(\times\) 10\({}^{6}\)** \\ \end{tabular} \\ \hline P 1 & VGG & None & CTC & 29.62 & 0.996 & 0.55 & 5.57 \\ \hline P 2 & VGG & None & Attn & 80.36 & 6.977 & 0.93 & 6.58 \\ \hline P 3 1 & VGG & BiLSTM & CTC & 69.03 & 1.993 & 0.89 & 8.45 \\ \hline P 4 & VGG & BiLSTM & Attn & 84.70 & 7.973 & 0.95 & 9.14 \\ \hline P 5 & RCNN & None & CTC & 28.55 & 4.983 & 0.55 & 1.88 \\ \hline P 6 2 & RCNN & None & Attn & 83.82 & 10.964 & 0.94 & 2.89 \\ \hline P 7 3 & RCNN & BiLSTM & CTC & 69.15 & 5.980 & 0.90 & 4.76 \\ \hline P 8 & RCNN & BiLSTM & Attn & 86.19 & 12.956 & 0.95 & 5.46 \\ \hline P 9 4 & ResNet & None & CTC & 53.41 & 2.990 & 0.78 & 44.28 \\ \hline P 10 & ResNet & None & Attn & 85.80 & 9.968 & 0.95 & 45.29 \\ \hline P 11 & ResNet & BiLSTM & CTC & 78.30 & 3.986 & 0.93 & 47.16 \\ \hline P 12 5 & ResNet & BiLSTM & Attn & 89.32 & 11.960 & 0.96 & 47.86 \\ \hline P 13 & ConvNeXt & None & CTC & 56.81 & 5.979 & 0.83 & 67.57 \\ \hline P 14 & ConvNeXt & None & Attn & 86.27 & 11.960 & 0.95 & 68.58 \\ \hline P 15 & ConvNeXt & BiLSTM & CTC & 79.24 & 6.976 & 0.93 & 70.45 \\ \hline P 16 & ConvNeXt & BiLSTM & Attn & 90.21 & 14.951 & 0.97 & 71.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recognition accuracy, normalized edit distances, and number of parameters of the network. Figure 19: Feature maps for partial networks. According to Table 1, we see the trade-off between accuracy and the number of parameters for all 16 different module combinations. The comparative results are shown more clearly in Figure 20. In Figure 20, we show the results of current text recognition models applied to Uyghur text recognition. Among them, R2 AM performs better, with accuracy rates of 83.82%; on the other hand, Rosetta uses ResNet for feature extraction but removes the BiLSTM layer, which leads it to perform poorly in the recognition network using CTC for prediction, with only 53.41%. In addition, the networks using VGG or RCNN for feature extraction, also with the BiLSTM layer removed in the sequence modeling phase, perform the worst in the network using CTC for prediction, with a recognition accuracy of only 29.62% or 28.55%, respectively. Therefore, we believe that in Uyghur recognition, if CTC is used for prediction, it is better to use the BiLSTM layer for sequence modeling. If the visual features output from the feature extraction layer are only directly decoded using CTC, the results will be unsatisfactory, which may be due to the fact that Uyghur is sticky, i.e., the preceding and following characters in a word are stuck together, thus requiring more textual contextual information. Compared to previous prominent approaches in Uyghur text recognition, which were primarily applied to Uyghur printed or scanned texts using models like CRNN, GRCNN, and RBA, we have achieved substantial progress in the realm of Uyghur script recognition.