## 3 Results and Discussion  
Table 1 shows the accuracy of each character used in the multimodal model. We trained each of the four characters through the Siamese network. The complexity of the characters was high in the order of '\(\mathbb{Q}\)', '\(\mathbb{Q}\)', '\(\mathbb{Q}\)', and '\(\mathbb{Q}\)'. Looking at the table, the lower the complexity of the character, the lower the accuracy, because the character had fewer characteristics. Therefore, we proceeded with the combination according to the complexity of the characters when combining the characters that would be used in the multimodal. Figure 6: Scatter plots of multimodal character pairs: (a) multimodal of the ‘\(\mathbb{Q}\)’ and ‘\(\mathbb{Q}\)’ pairs; (b) multimodal of the ‘\(\mathbb{Q}\)’, ‘\(\mathbb{Q}\)’ and ‘\(\mathbb{Q}\)’ pairs. When combining more characters, we wanted to prove the effectiveness of the verification by combining the characteristics of lower complexity. We experimented with two types of kernels of the SVM model, linear and RBF, and the difference between the two kernels was not large because the distribution of data was divided in a balanced manner. If the distribution was lower, the RBF kernel performed better because the curve or surface provided a difference to the finer details in denoting the boundary. Figures 7 and 8 show the data classified using linear and RBF kernels, and visually show the classification boundaries. \begin{table}
\begin{tabular}{c c c c c} \hline \hline Accuracy (\%) & \multicolumn{1}{c}{\(\mathfrak{A}\)} & \multicolumn{1}{c}{\(\mathfrak{A}\)} & \multicolumn{1}{c}{\(\mathfrak{A}\)} & \multicolumn{1}{c}{\(\mathfrak{A}\)} \\ \cline{2-5}  & 82.66\% & 81.36\% & 78.54\% & 78.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Verification accuracy of single character. Figure 7: Scatter plots of multimodal of two characters with a classifier: (a) linear kernel boundary; (b) RBF kernel boundary. Table 2 shows the accuracy compared using the RBF kernel according to the parameter C, which was 100, and the number of characters. The average multimodal accuracy was confirmed to be over 88%. This meant that the multimodal accuracy was higher than the single-character accuracy. For more than two characters, the reason for the high accuracy was that the predicted values, previously classified as one threshold, were classified in more detail through nonlinear thresholds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Accuracy (\%)} & \multicolumn{4}{c}{**Number of Characters**} \\ \cline{2-5}  & One 1  & Two 2  & Three 3  & Four 4  \\  & 80.14\% & 87.65\% & 89.22\% & 90.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Verification accuracy through multimodal. Figure 8: Scatter plots of multimodal of three characters with a classifier: (**a**) linear kernel boundary; (**b**) RBF kernel boundary. Figure 9 shows the receiver operating characteristics (a) and the area under the curve (AUC) of the test results (b) [15]. Looking at (a), we saw that the larger the number of char-acters, the greater the AUC, and (b) shows the AUC value according to the number of characters. Figure 10 shows an example of the multimodal combination and the results that this paper intended to show. Figure 10a shows an example of combining the two characters. The character 'gl' on the left had a predicted value of \(-3.16\) and a predicted label of \(0\) was incorrectly predicted. However, combining the correctly predicted 'gl' characters on the right through multimodal showed that the two characters were correctly predicted. In Figure 10b, as in Figure 10a, we saw that the incorrectly predicted 'gl' and 'gl' characters combined with the correctly predicted 'gl' characters were correctly predicted. Figure 10c shows the same results. The key point was that multimodal approaches can be used to correctly predict previous incorrectly predicted outcomes. Figure 9: An example of receiver operating characteristic curve (ROC) and AUC: (**a**) ROC curve; (**b**) AUC by number of characters.