## 4 Experiments
### Effect and Analysis of Character Vowel Size  
In the process of writing a Tibetan historical document, it is not completely written by a single person but by multiple people working together. Everyone's writing style is different, and there are a large number of Tibetan characters whose upper vowel part of the image is wider than the part below the baseline. Experiments show that, in this case, the recognition rate is not high because the samples are directly fed into the model for recognition, and it is necessary to normalize the upper vowel (as shown in Figure 12) to improve the recognition rate. When size normalization is performed for upper vowels, the proportion of upper vowels significantly affects the recognition rate. We first performed an experimental analysis for the '\(\mathbb{R}^{\prime}\) character sample in Figure 12; the results are shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Identification** & **Probability of** & **Information** \\  & **Results** & **Top1** & **Entropy** \\ \hline Before vowel normalization & \(\mathbb{R}\) & 84.60\% & 0.71 \\ After vowel normalization & \(\mathbb{R}\) & 87.49\% & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of recognition before and after normalization of vowels on ’\(\mathbb{R}^{\prime}\) characters. Figure 11: Down-CNN model training process. Figure 12: \(\mathbb{R}^{\prime}\) diagram of comparison before and after vowel normalization on character samples. (**a**) Before normalization. (**b**) After normalization. From Table 2, we can see that the vowel of the '&' character sample was incorrectly identified as '&' before normalization with a Top1 probability of 84.6%, and correctly identified after normalization with a Top1 probability of 87.49% were identified as '&'. The information entropy of the image before normalization is 0.71, and the information entropy of the image after normalization is increased to 0.93. Since the character images used are white as the foreground color and black as the background color, the information of the characters is expressed as white pixels, and for the images without upper vowel normalization, there is a larger part of the area that is background color, and this area does not carry the information features of the characters. This part of the image, through the convolutional neural network when training recognition, consumes more operations and storage. However, this part of the calculation and the results do not obtain the character feature information. Through the normalization of the upper vowel size, it can reduce the background information in the image, representing the character white detail information amplified. In this way, the calculation can obtain more information related to the character features. The experiment shows that the recognition accuracy will be higher if the ratio of upper vowels is reduced and the information entropy of the image is increased. In order to determine under what conditions the normalization of the upper vowel leads to the highest recognition rate of the recognition system, experiments were performed with different upper vowel proportions. For all character sample data sets, the upper vowel normalization experiment was carried out on the character images with the upper vowel above and below the baseline. In order to obtain the character upper and lower ratio thresholds that make the best recognition rate, this paper selects different proportion threshold values for normalization training recognition and analyzes the recognition rate. The results are shown in Table 3.  
As can be seen from Table 3, with the increase of the selected proportion threshold, the recognition rate decreases gradually. When the selected proportion threshold is 1, the recognition rate is the highest. The overall recognition rate is 84.88%, 2.63% higher than the original CNN network. It is analyzed that the normalization of the upper vowel can increase the entropy of character information on the image. It helps to improve the recognition rate.