# Cascaded Dual-Inpainting Network for Scene Text
## 4 Experiments and Discussion
### Comparison with State-of-the-Art Approaches  
Comparative experiments were conducted on the TII-STsyn20k test set [30], benchmarking the proposed CDINST model against two state-of-the-art incomplete text inpainting methods: TWINST [29] and GSDM-SPM [30], which specifically focus on reconstructing text foregrounds. Tables 1-3 present comparative evaluations of text recognition accuracy (\(A_{w}\) and \(A_{c}\)) and image quality metrics (PSNR and SSIM) between existing methods and the proposed CDINST model on the test set. The inpainting effectiveness was benchmarked using multiple text recognition algorithms: CRNN [34], ASTER [33], ABINet [32], RobustScanner [35], SAR [36], and SVTR [37]. The experimental results demonstrate that CDINST achieves an average improvement of +11.94% in \(A_{w}\), +4.79% in \(A_{c}\), and +5.69% in character-level recognition recall over state-of-the-art methods, along with average PSNR gains of +0.075 dB PSNR and SSIM average improvements of +0.0742. Given the comparative results, CDINST can significantly enhance the inpainting capability for incomplete scene text. Several examples are presented to demonstrate the performance of the proposed method. Figure 2 illustrates scene text inpainting and recognition results, highlighting the capability of the proposed method to reconstruct the binary foreground of incomplete scene text. On the test set, it was demonstrated that comparative text inpainting results were achieved across different methods (TWINST, GSDM-SPM, and the proposed module \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **GT Structure** & **CorImg** & **TSINIT** & **GSDM-SPM** & **CDINST** \\ \hline CRNN [34] & 95.83 & 5.74 & 45.84 & 49.14 & **62.12** \\ ASTER [33] & 96.98 & 10.18 & 62.12 & 62.12 & **65.40** \\ ABINET [32] & 97.23 & 21.40 & 61.20 & 63.89 & **71.73** \\ Robust-Scanner [35] & 97.77 & 16.13 & 50.05 & 53.62 & **64.62** \\ SAR [36] & 97.83 & 15.57 & 49.96 & 53.25 & **64.34** \\ SVTR [37] & 98.16 & 23.39 & 55.29 & 60.26 & **67.74** \\ \hline PSNR & - & - & 14.14 & **18.75** & 16.52 \\ SSIM & - & - & 0.7294 & 0.8086 & **0.8432** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Word-level recognition accuracy (\(A_{w}\)) comparison across different methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **GT Structure** & **CorImg** & **TSINIT** & **GSDM-SPM** & **CDINST** \\ \hline CRNN [34] & 99.43 & 60.30 & 84.58 & 86.31 & **90.59** \\ ASTER [33] & 99.44 & 56.46 & 83.64 & 85.90 & **90.57** \\ ABINET [32] & 99.56 & 73.10 & 88.63 & 89.77 & **92.40** \\ Robust-Scanner [35] & 99.64 & 68.07 & 84.06 & 86.10 & **90.72** \\ SAR [36] & 99.68 & 65.95 & 83.03 & 85.20 & **90.45** \\ SVTR [37] & 99.75 & 73.88 & 88.25 & 90.43 & **91.95** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Character-level recognition accuracy (\(A_{c}\)) comparison across different methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **GT Structure** & **CorImg** & **TSINIT** & **GSDM-SPM** & **CDINST** \\ \hline CRNN [34] & 99.46 & 41.99 & 86.31 & 85.00 & **90.52** \\ ASTER [33] & 99.32 & 42.41 & 85.90 & 85.46 & **90.73** \\ ABINET [32] & 99.49 & 55.64 & 88.68 & 87.83 & **91.95** \\ Robust-Scanner [35] & 99.52 & 57.26 & 86.10 & 85.66 & **90.60** \\ SAR [36] & 99.54 & 55.74 & 85.20 & 85.22 & **90.42** \\ SVTR [37] & 99.61 & 58.08 & 90.43 & 87.93 & **91.66** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Character-level recognition recall (\(Recall_{c}\)) comparison across different methods. CDINST). The CRNN recognition model was employed to evaluate the performance of these methods. Figure 2a-d, respectively, show corrupted scene text images; ground truth structure images; inpainting results generated by TWINST, GSDM-SPM, and the proposed CDINST method; and corresponding recognition results. The comparative experiments demonstrate that the proposed method achieves more accurate text structures than TWINST and GSDM-SPM.