## 3 Structure of Recognition
### Feature Extraction Stage  
In this study, we used four different Convolutional Neural Network structures, VGG, RCNN, ResNet, and ConvNeXt, to extract network features. The VGG network is a Convolutional Neural Network consisting of multiple convolutional layers and several fully connected layers. In the classical text recognition algorithm CRNN, the VGG network is the network structure chosen for the CNN phase. Adjustments were made for better suitability to linguistic texts. A rectangular window of \(1\,\times\,2\) size is used in the third and fourth maxpooling layers to generate feature maps with larger widths instead of the traditional square maxpooling window. As shown in Figure 2, after inputting a normalized \(100\,\times\,32\) Uyghur image, the VGG network generates a feature map with a smaller width and height and more channels and finally outputs a visual feature map with 512 channels (24 width and 1 height), which is then input into the sequence model for the next stage of training. The RCNN is an improved feature extraction network using recurrent convolutional layers (RCLs). With the same number of parameters, RCLs increase the depth of the CNN and produce a more compact feature response than the CNN. In order to be more suitable for text recognition and take into account different perceptual fields, [PERSON] et al. (2018) further improved the RCNN by adding a gate to the recurrent convolutional layer (RCL), which is a key component of the RCNN, so as to construct a gated recurrent convolutional layer (GRCL) to balance the feedforward information and recurrent information and to finally constitute the GRCNN network. The gating unit is computed as (1):  
\[G(t)=\left\{ \begin{array}{cc}0&t=0\\ \sigma_{sig}\left(BN\left(w_{g}^{f}(u(t))\right)+BN\left(w_{g}^{r}(x(t-1)) \right)\right)&t>0\end{array} \right. \tag{1}\]  
where \(t\) represents the time scale and \(w_{g}^{f}\) and \(w_{g}^{r}\) denote the feedforward weights and the cyclic weights of the gate for two \(1\,\times\,1\) kernels, which are convolved with the feedforward input and the cyclic convolution input, respectively. Then, after batch normalization and the sigmoid function, \(G(t)\) is generated, which indicates the strength of information inflow in the hidden state. \(``x(t)\"\) is then calculated from (2), where \(w^{f}\) and \(w^{r}\) also use a \(1\,\times\,1\) convolution kernel to calculate the weights, and finally \(x(t)\) is calculated by batch  
Figure 1: Three-stage Uyghur identification structure. Figure 2: VGG feature extraction network. normalization and the ReLU function. When \(G(t)\) is 1, all the information from the previous \(t\)-1 steps is retained. When \(G(t)\) is 0, the information from the previous hidden state is discarded and only the input \(u(t)\) at moment t is considered. \[x(t)=\left\{ \begin{array}{ll}\sigma_{\text{ReLU}}(BN(w^{f}*u(t)))&t=0\\ \sigma_{\text{ReLU}}(BN(w^{f}*u(t))+BN(BN(w^{r}*x(t-1))\odot G(t)))&t>0\end{array} \right. \tag{2}\]  
In the overall feature extraction network, the GRCL layer is used to replace the convolutional layers, replacing the second, third, fourth and fifth convolutional layers with three GRCL layers with five iterations, of which the convolutional kernel size is \(3\ \times\ 3\). The final feature extraction network outputs a feature map with a channel number, height and width of \(512\ \times\ 1\ \times\ 26\). The overall network feature map is shown in Figure 3.  
ResNet, a CNN network with residual connections, moderates two problems of network gradient explosion and recognition accuracy saturation which lead to a rapid decrease in accuracy, learning the difference between the target and input values instead of the complete output so as to make deeper recognition network training possible. We used ResNet to improve the feature extraction network part of our three-stage Uyghur recognition structure and used an attention mechanism in the subsequent decoding prediction stage.