## 4 Experiments
### Ablation Experiments  
In this ablation study, a detailed performance assessment of different combinations of model modules was carried out, analyzing the contributions of each module to the text recognition task. The experimental results, as shown in Table 3, use precision, recall, and F-score as evaluation metrics, observing the improvement effect on model performance as different modules were sequentially added. Without any additional modules, MTF-STTR achieved precision, recall, and an F-score of 84.3%, 75.2%, and 80.6%, respectively. On this basis, individual tests were performed by separately incorporating the DB, IDFP, and TFM modules.  
Subsequently, the efficacy of dual-module combinations was tested. The combination of DB and IDFP resulted in an increased F-score to 85.9%, demonstrating strong synergistic effects. The combination of DB and TFM yielded an F-score of 85.4%, while the combination of IDFP and TFM showed the best performance, reaching an F-score of 87.3 and indicating a significant promotion of text recognition performance when IDFP and TFM act in concert.  
Ultimately, when all three modules--DB, IDFP, and TFM--were fully integrated, the model's precision reached 89.9%, its recall rose to 85.6%, and the F-score achieved 87.8%, delivering optimal performance. This indicates that the collaborative action of the DB, IDFP, and TFM modules has a significant impact on enhancing the model's text recognition capabilities. In summary, the ablation experiment conducted in this study effectively validated the contributions of individual modules to the enhancement of text recognition performance. By utilizing different modules singly and in combination, the experiment has illustrated the impact of each module on the model's performance, as well as the synergistic effects among them. This provides crucial empirical support for the optimization of the model architecture, further demonstrating the effectiveness and optimization potential of the proposed framework. Additionally, it offers invaluable experience and reference for future applications in similar tasks.  
To better showcase the effectiveness of the proposed method in erasing logo text in practical applications, Figure 8 provides a detailed depiction of the method's performance in real-life scenarios. This visualization allows viewers to observe the outcomes of applying the method to remove logo text from actual-environment contexts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MSE** & **PSNR** & **pEPs** & **pCEPs** \\ \hline EnsNet [27] & 0.0021 & 29.5 & 0.0069 & 0.002 \\ EraseNet [10] & **0.0002** & 38.3 & 0.0048 & 0.0004 \\ Pix2 Pix [32] & 0.0027 & 26.8 & 0.0473 & 0.0244 \\ Bain [33] & 0.0159 & 20.8 & 0.1021 & 0.5996 \\ MTF-STTR & 0.0003 & **40.1** & **0.004** & **0.0003** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of various networks on SCUT-Syn dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DB** & **IDFP** & **TFM** & **Precision** & **Recall** & **F-Score** \\ \hline  & & & 84.3 & 75.2 & 80.6 \\ ✓ & & & 86.1 & 81.4 & 84.5 \\  & ✓ & & 87.6 & 80.3 & 84.9 \\  & & ✓ & 85.8 & 81.3 & 84.1 \\ ✓ & ✓ & & 88.2 & 83 & 85.9 \\ ✓ & & ✓ & 86.8 & 82.9 & 85.4 \\  & ✓ & ✓ & 88.7 & 84.2 & 87.3 \\ ✓ & ✓ & ✓ & **89.9** & **85.6** & **87.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results of MTF-STTR on CTW1500 dataset.