## 4 Experiments
### Overall Performance Analysis  
There are two factors that affect the overall performance of the model. On the one hand, although the synthetic and real samples are similar, they are not completely consistent. Because the synthesis process of the synthetic sample itself is the result of the stacking of the images of the upper and lower parts, the location information of each part of the real sample is not completely consistent with the location information of the synthetic sample. In order to reduce this inconsistency, the character image can be divided into the upper and lower parts and identified separately. On the other hand, there are great similarities between different Tibetan characters, which also poses challenges for recognition models. The model determines the classification according to the calculated probability. If the probability corresponding to the preferred character is close to the probability of the second-selected character, it indicates that the characteristics of the preferred character and the second-selected character are close and the font is similar. If the identification result is based on the preferred character, it will likely be wrong. At this time, fine-grained features need to be further extracted before recognition; that is, the characters are divided into upper and lower parts for feature extraction. However, the experiment also shows that the recognition rate is not the highest when all the character images are recognized by splitting up and down. To determine when to split a character image, we use a probability threshold to control whether to split or not. The probability threshold is defined as the ratio of the probability corresponding to the preferred character (denoted as Max1) to the probability corresponding to the second selected character (denoted as Max2). In order to improve the overall recognition performance, the influence of proportional threshold parameters on the recognition rate is analyzed in the experiment. In order to obtain a suitable probability threshold, we first roughly selected probability thresholds of different orders of magnitude for the experiment. Initially, the threshold range is determined. Then, this threshold range is subdivided to finally determine the appropriate threshold. The initial selection of probability thresholds includes 2, 10, 50, 100, 200, 500 and 1000. The results are shown in Table 4.  
As can be seen from Table 4, the recognition rate is higher for different probability threshold cases with upper vowel ratio thresholds of 1 and 1.1. The recognition rate is higher for probability thresholds between 10 and 100 under different upper vowel proportion thresholds. This determines the range in which the optimal threshold is located. In order to get the accurate optimal threshold, the experiment is carried out after further subdividing the threshold range. Further experimental calculations lead to optimal probability thresholds: First, the proportional thresholds are fixed to the two values that make the preferred accuracy higher, i.e., an upper vowel proportional threshold of 1.0 and an upper vowel proportional of 1.1. Then, eight values are selected between 10 and 100, which are 10, 15, 18, 20, 30, 40, 50 and 60 in order. Finally, the model was used to identify and compare the preferred recognition rate of 11,1932 real Tibetan historical document character image sample sets under the corresponding proportional threshold and probability threshold. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Probability** **Threshold**} & \multicolumn{4}{c}{**Proportional Threshold**} \\ \cline{2-5}  & **1** & **1.1** & **1.4** & **1.7** \\ \hline
2 & 86.15\% & 85.75\% & 85.09\% & 84.93\% \\
10 & 87.16\% & 86.96\% & 86.71\% & 86.67\% \\
50 & 87.17\% & 87.07\% & 86.94\% & 86.91\% \\
100 & 86.84\% & 86.92\% & 86.83\% & 86.81\% \\
200 & 86.86\% & 86.80\% & 86.72\% & 86.70\% \\
500 & 86.66\% & 86.62\% & 86.57\% & 86.53\% \\
1000 & 86.56\% & 86.52\% & 86.48\% & 86.44\% \\ \hline \hline \end{tabular}
\end{table}