## 4 Experiments
### Overall Performance Analysis

Table 4: Top1 recognition rates corresponding to different proportional thresholds and probability thresholds. Through the experimental analysis, it can be seen from Table 5 that the Top1 accuracy reaches the highest value of 87.27% when the proportional threshold is 1.0 and the probability threshold takes the value of 20. To further illustrate the performance of the method in this paper, we compared it with SVM and single CNN methods and analyzed the performance of the method in this paper under different parameter environments. In the experiment, the model cross-validation method is the method of sample random division. The samples are randomly divided into training, verification and test sets. The comparison results of the three methods are shown in Table 6.  
As can be seen from Table 6, the Top1, Top5 and Top10 recognition rates of the model method (UD-CNN) in this paper are higher than those of other methods. The method introduces a proportional threshold and probability threshold in the recognition process. This enables the extraction of features from the Tibetan character images as a whole, above the baseline and part of the images below the baseline. It improves the recognition rate of the sample set of authentic Tibetan ancient character images. In order to show the recognition effect of the proposed model, the recognition results of three-character images by different algorithms are given, as shown in Table 7.  
As can be seen from Table 7, the recognition results of the proposed model for the three character images are correct, while the recognition results of the SVM and Single CNN models are wrong. The wrong result is similar to the correct result font. The proposed algorithm can obtain finer-grained distinguishing features of character images by splitting them up and down so that the final recognition result is correct. In addition, the real images of Tibetan characters are segmented from the images of historical documents. Due to the influence of noise and binarization algorithm, the real \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Top1** & **Top5** & **Top10** \\ \hline SVM & 80.25\% & 86.76\% & 91.87\% \\ Single CNN & 82.88\% & 90.81\% & 92.86\% \\ Model of this paper (UD-CNN) & 87.27\% & 92.94\% & 94.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The recognition accuracy of different methods. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Top1** & **Top5** & **Top10** \\ \hline SVM & 80.25\% & 86.76\% & 91.87\% \\ Single CNN & 82.88\% & 90.81\% & 92.86\% \\ Model of this paper (UD-CNN) & 87.27\% & 92.94\% & 94.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Identification results of different methods. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Top1** & **Top5** & **Top10** \\ \hline SVM & 80.25\% & 86.76\% & 91.87\% \\ Single CNN & 82.88\% & 90.81\% & 92.86\% \\ Model of this paper (UD-CNN) & 87.27\% & 92.94\% & 94.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top1 recognition rates corresponding to probability thresholds 10–60 for proportional thresholds of 1.0 and 1.1. Tibetan character image itself contains noise information, including the edge of the stroke is not smooth, some strokes are missing, redundant strokes, etc., and some characters and images are mixed in a variety of situations, as shown in Figure 13.  
This noise phenomenon is inevitable. The above recognition rate of the model is obtained on the real images containing noise, which also shows that our proposed model has strong anti-noise ability. In order to further illustrate the anti-noise ability of the proposed algorithm, we added Gaussian noise (mean 0, variance 0.01) to the real images, and the recognition rate of the proposed model for the character images with the new noise only decreased a little, and the Top1 only decreased by 0.25%. This shows that our algorithm has good anti-noise ability.