## 5 Results and Discussion
### Model Interpretability  
In this section, we present Saliency Maps [34] to present an interpretation of our model. A Saliency Map is an interpretive tool for visualizing deep learning models. It measures the sensitivity of the model's output to the input by calculating the gradient of the input image. It then maps this information onto the input image to produce an image of the same dimensions as the input image, where the value of each pixel indicates the effect of that  
Figure 14: Figure showing the effect of data augmentation via different methods. (**a**) Original picture. (**b**) Stochastic affine transformation. (**c**) Gaussian noise. (**d**) Elastic transformation. pixel on the model predictions. We analyzed the prominence of the image and how much influence each character has on the model prediction by drawing Saliency Maps. In Figure 15, we can find that each pixel in the Saliency Map drawn by calculating the degree of influence of the whole word on the prediction indicates the degree of influence on the prediction. In this map, pixels that may be true values and pixels that have a positive effect on the prediction are indicated by brightly colored pixels, especially at the inflection points of the text in the image. The upper and lower points of the characters in the text are highlighted, and on the whole, it can be seen that the pixels that have a positive effect on the prediction of the model make up images that are basically the same as the textual values. As shown in Figure 16, through the Saliency Maps of the influence of each character on the prediction, we found that a single character making a prediction will appear as a brighter pixel at the position where it is supposed to be in the picture, and darker in other places. Since Uyghur is written from right to left, the order of the predicted characters corresponds to the pictures getting brighter from right to left. In order to compare how different data points are correctly predicted from one another, we used LIMEs (Local Interpretable Model-Agnostic Explanations) [35] to effectively explain how the model works by selectively adding noise to certain parts of the image. We have selected LIME visualization images of some of the characters that represent the predicted meaning of the word indecision. This is shown in Figure 17. We find that the model focuses its attention between the character itself and its neighboring characters, which effectively explains that Uyghur is adhesive, and that to correctly recognize a character it is also necessary to focus on its neighboring characters. Figure 16: Saliency Maps for each character for predicting the degree of influence. Figure 15: Saliency Map of the whole word for predicting the degree of influence. We also used the SHAP (Shapley additive explanation) [36] algorithm, which has performed extremely well in classification models, to conduct an interpretable analysis of the recognition model; not only does the SHAP model help to explain the contribution of certain pixels to the model output, but by visualizing the SHAP values of different images, it is also possible to identify patterns and differences in the model's decision making across different inputs. We analyzed the significance of all the characters of the word \"_a_\",\(b\),\(c\),_d_\" and plotted an SHAP heat map. This is shown in Figure 18. We show the prediction results for the top two predicted values separately, and we find that for each character, the results predicted by the first pair of plots are correct, while the results of the second plot are all wrong, but the characters represented by the second plot are morphologically extremely close to the correct results; thus, we believe that our model has a very reliable ability to recognize the correct character through minute details. Afterwards, we plotted the attention feature maps for each layer of the network, again for the sake of model interpretability.