## 5 Results and Evaluation  
The results are presented in Table 1. The classifier trained on a supervised contrastive model's downstream task showed better test accuracy. The classifier achieved the highest test accuracies of 99.76%, 99.73%, and 99.31% for Korean, English, and Chinese fonts, respectively, with supervised contrastive loss when trained using 100% of the images in the labeled dataset. Notably, the supervised contrastive classifier achieved such high accuracy in only 50 epochs, which is significantly fewer epochs than the fully supervised classifier, which was trained for 500 epochs. When trained with only 10% of the labeled images, the classifier achieved a test accuracy of 98.1%, 98%, and 97.8% for Korean, Chinese, and English font styles, respectively, in fewer training iterations than a fully supervised classifier trained on the entire dataset. These results demonstrate the effectiveness of using contrastive learning for font-style classification in scenarios where labeled data are scarce, which can achieve high accuracy with few labeled images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Language** & **Method** & **Epochs** & \begin{tabular}{c} **Labeled** \\ **Dataset Size** \\ \end{tabular} & \
\begin{tabular}{c} **Training** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Test** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{7}{*}{Chinese} & Supervised & 500 & 65,000 & 99.90 & 99.38 \\ \cline{2-6}  & \multirow{4}{*}{NT-Xent} & \multirow{4}{*}{50/50} & 65,000 (100\%) & 99.23 & 98.67 \\ \cline{3-6}  & & & 32,500 (50\%) & 99.00 & 98.58 \\ \cline{3-6}  & & & 6500 (10\%) & 98.34 & 98.13 \\ \cline{3-6}  & \multirow{4}{*}{Triplet} & \multirow{4}{*}{50/50} & 65,000 (100\%) & 99.78 & 99.11 \\ \cline{3-6}  & & & 32,500 (50\%) & 99.63 & 98.98 \\ \cline{3-6}  & & & 6500 (10\%) & 98.93 & 98.27 \\ \cline{3-6}  & \multirow{4}{*}{Supervised Contrastive} & \multirow{4}{*}{50/50} & 65,000 (100\%) & 99.82 & 99.31 \\ \cline{3-6}  & & & 32,500 (50\%) & 99.53 & 99.14 \\ \cline{3-6}  & & & 6500 (10\%) & 98.45 & 98.00 \\ \hline \multirow{7}{*}{English} & Supervised & 500 & 52,000 & 100 & 99.95 \\ \cline{3-6}  & \multirow{4}{*}{NT-Xent} & \multirow{4}{*}{50/50} & 52,000 (100\%) & 99.35 & 98.10 \\ \cline{3-6}  & & & 26,000 (50\%) & 99.15 & 98.37 \\ \cline{3-6}  & & & 5200 (10\%) & 98.90 & 98.45 \\ \cline{3-6}  & \multirow{4}{*}{Triplet} & \multirow{4}{*}{50/50} & 52,000 (100\%) & 99.85 & 99.17 \\ \cline{3-6}  & & & 52,000 (100\%) & 98.68 & 98.40 \\ \cline{3-6}  & & & 5200 (10\%) & 99.33 & 99.20 \\ \cline{3-6}  & \multirow{4}{*}{Supervised Contrastive} & \multirow{4}{*}{50/50} & 52,000 (100\%) & 99.91 & 99.73 \\ \cline{3-6}  & & & 26,000 (50\%) & 99.53 & 99.07 \\ \cline{3-6}  & & & 5200 (10\%) & 98.10 & 97.80 \\ \hline \multirow{7}{*}{Korean} & Supervised & 500 & 35,000 & 100 & 100 \\ \cline{3-6}  & & & 35,000 (100\%) & 99.35 & 99.00 \\ \cline{3-6}  & & & 17,500 (50\%) & 99.15 & 98.13 \\ \cline{3-6}  & & & 3500 (10\%) & 98.90 & 98.05 \\ \cline{3-6}  & \multirow{4}{*}{Triplet} & \multirow{4}{*}{50/50} & 35,000 (100\%) & 99.53 & 99.07 \\ \cline{3-6}  & & & 3500 (10\%) & 99.10 & 98.95 \\ \cline{3-6}  & & & 35,000 (100\%) & 100 & 99.76 \\ \cline{3-6}  & \multirow{4}{*}{Supervised Contrastive} & \multirow{4}{*}{50/50} & 17,500 (50\%) & 99.68 & 99.40 \\ \cline{3-6}  & & & 3500 (10\%) & 98.33 & 98.10 \\ \cline{3-6} \cline{3-6}  & & & & 35,000 (100\%) & 99.53 & 99.07 \\ \cline{3-6}  & & & 3500 (10\%) & 99.10 & 98.95 \\ \cline{3-6}  & & & 35,000 (100\%) & 100 & 99.76 \\ \cline{3-6}  & & & 17,500 (50\%) & 99.68 & 99.40 \\ \cline{3-6}  & & & 3500 (10\%) & 98.33 & 98.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of training and test accuracies of classifiers trained on NT-Xent, triplet, and supervised contrastive approaches with a fully supervised classifier used as a baseline. Figure 5 shows the t-SNE Projections of Chinese, English, and Korean embeddings vectors models trained using NT-Xent, Triplet loss and supervised contrastive loss.