# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 5 Experimental Results
### Comparison with SOTA Methods
#### 5.2.1 Korean Results  
Quantitative Results: Table 1 presents the quantitative performance of Patch-Font and baseline methods (FUNIT, DM-Font, LF-Font, MX-Font, and FM-GAN) on the Korean handwritten (HW) and printed datasets, evaluated for both seen characters (SCs) and Unseen characters (UCs). For printed SCs, Patch-Font achieves the highest Acc (C) at 98.43%, surpassing the second-best method, DM-Font (96.83%). In terms of Acc (S), Patch-Font records 78.19%, closely following DM-Font (79.19%) but outperforming other baselines, including MX-Font (63.28%) and LF-Font (75.60%). Patch-Font achieves the lowest LPIPS score of 0.109, indicating better perceptual similarity than all baselines, and the highest SSIM (0.785). Its FID score of 73.3 further reflects the best alignment between generated and real font distributions. For printed UCs, Patch-Font outperforms all baselines with an Acc (C) of 97.30% and an Acc (S) of 76.26%, demonstrating its robustness in generating unseen characters. Its LPIPS (0.132) and FID (91.2) remain superior, confirming its generalization ability and perceptual fidelity. For handwritten SCs, Patch-Font achieves the highest Acc (C) of 99.23% and an Acc (S) of 79.28%, surpassing DM-Font (78.30%) and LF-Font (73.36%). Patch-Font's LPIPS score of 0.109 and FID score of 92.0 reflect its ability to generate perceptually realistic handwritten characters. Its SSIM score of 0.785 further indicates strong structural fidelity, closely aligning with the ground truth. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (C) \(\uparrow\)** & **Acc (S) \(\uparrow\)** & **LPIPS \(\downarrow\)** & **SSIM \(\uparrow\)** & **FID \(\downarrow\)** \\ \hline \multirow{6}{*}{Printed SCs} & FUNIT & 85.71 & 48.14 & 0.140 & 0.677 & 116.0 \\  & DM-Font & 96.83 & **79.19** & 0.149 & 0.778 & 76.8 \\  & LF-Font & 89.60 & 75.60 & 0.169 & 0.548 & 93.4 \\  & MX-Font & 96.28 & 63.28 & 0.118 & 0.758 & 84.0 \\  & FM-GAN & 79.14 & 74.56 & 0.225 & 0.692 & 74.1 \\  & Patch-Font & **98.43** & 78.19 & **0.109** & **0.785** & **73.3** \\ \hline \multirow{6}{*}{Printed UCs} & FUNIT & 74.89 & 28.14 & 0.160 & 0.477 & 133.0 \\  & DM-Font & 95.00 & 75.00 & 0.150 & 0.750 & 80.0 \\  & LF-Font & 97.10 & 43.57 & 0.169 & 0.481 & 106.3 \\  & MX-Font & 91.32 & 59.80 & 0.141 & 0.686 & 95.8 \\  & FM-GAN & 80.00 & 68.00 & 0.210 & 0.680 & 78.0 \\  & Patch-FONT & **97.30** & **76.26** & **0.132** & **0.758** & **91.2** \\ \hline \multirow{6}{*}{HW SCs} & FUNIT & 75.60 & 18.28 & 0.240 & 0.677 & 131.2 \\  & DM-Font & 94.00 & 78.30 & 0.153 & 0.770 & 84.0 \\ \cline{1-1}  & LF-Font & 95.71 & 73.36 & 0.169 & **0.791** & 116.0 \\ \cline{1-1}  & MX-Font & 96.28 & 66.28 & 0.118 & 0.758 & 114.0 \\ \cline{1-1}  & FM-GAN & 78.00 & 70.00 & 0.220 & 0.680 & 78.0 \\ \cline{1-1}  & Patch-Font & **99.23** & **79.28** & **0.109** & 0.785 & **92.0** \\ \hline \multirow{6}{*}{HW UCs} & FUNIT & 74.89 & 8.14 & 0.260 & 0.477 & 153.0 \\ \cline{1-1}  & DM-Font & 93.45 & 70.00 & 0.163 & 0.736 & 87.0 \\ \cline{1-1}  & LF-Font & 97.10 & 58.23 & 0.169 & 0.713 & 132.0 \\ \cline{1-1}  & MX-Font & 92.58 & 65.86 & 0.141 & 0.686 & 119.0 \\ \cline{1-1}  & FM-GAN & 80.00 & 68.00 & 0.210 & 0.680 & 78.0 \\ \cline{1-1}  & Patch-Font & **97.16** & **78.57** & **0.132** & **0.758** & **98.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison on Korean handwritten and printed datasets. All the methods are evaluated on both seen and unseen characters for unseen fonts during training. \(\uparrow\) indicates that a higher value is better, whereas \(\downarrow\) indicates that a lower value is better. For handwritten UCs, Patch-Font continues to outperform other methods with an Acc (C) of 97.16% and an Acc (S) of 78.57%. Its LPIPS score increases slightly to 0.132, but it remains the best among the baselines. Patch-Font also achieves the lowest FID (98.0), indicating its ability to generate realistic handwritten fonts for unseen characters. Its SSIM score of 0.758 remains competitive, demonstrating robust structural preservation.