# Twin-FPN++: Rwin Transformer with Feature Pyramid Network for Dense Scene Text Spotting
## 4 Experiment
### Evaluation Results
#### 4.3.1 Evaluation of Detection Task  
We use the standard recall, precision, and F-measure metrics to evaluate the performance of detection. We have compared six state-of-the-art methods with the proposed RWIN-FPN++ approach. All six methods have publicly released code available for download. We retrain these six approaches with the same protocol described in Section 4.1. Table 1 shows that our Rwin-FPN++ method can achieve an F-measure of 79% and outperform all other methods in F-measure by at least 2.8%. These results prove that our Rwin-FPN++ approach can detect the dense scene text accurately. It is worth noting that the F-measure of all methods are lower than that of the five public datasets above, which indicates that our dense scene text is more difficult than the current public datasets. Figure 6 shows some qualitative comparisons of detection performance between state-of-the-art methods and the Rwin-FPN++. Compared with state-of-the-art methods, our Rwin-FPN++ network is more effective at detecting dense scene texts with various bends, occlusion, and lighting. Figure 7 shows other detection results of the Rwin-FPN++ network.  
Figure 5: Some examples of the dense scene text dataset from the wiring of the terminal block of the substation panel cabinet.  
We also compare our method to previous methods on the public dataset Total-Text [31] and CTW1500 [30]. Total-Text contains 1555 images with multiple curved, multi-oriented,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F-Measure** \\ \hline EAST [6] & 53.2 & 48.5 & 50.7 \\ Textspotter [37] & 72.4 & 70.9 & 71.6 \\ Textboxes++ [38] & 73.2 & 71.9 & 72.6 \\ PSENet [8] & 73.7 & 70.1 & 71.8 \\ Pan++ [10] & 76.6 & 71.1 & 73.7 \\ ABCNet v2 [5] & 79.7 & 73.1 & 76.2 \\ Our approach & **82.2** & **76.1** & **79.0** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection comparison with state-of-the-art methods on our dense scene text dataset.  
Figure 6: Qualitative comparisons of detection performance between state-of-the-art methods and the Rwin-FPN++. Compared with state-of-the-art methods, our Rwin-FPN++ network is more effective at detecting dense scene texts with various bends, occlusion, and lighting.  
Figure 7: Some other detection results of the Rwin-FPN++ network.  
and horizontal text regions; 1255 images were used for training and the other 300 images for testing. All the text instances are labeled with eight vertices of the quadrangle, as well as corresponding transcriptions. CTW1500 [30] consists of 1500 images, which are harvested from the internet and some image libraries. This dataset provides 10,751 cropped text instances, including 3530 curved and multi-oriented text instances. Tables 2 and 3 show that our Rwin-FPN++ method can achieve an F-measure of 88% and 85.7% and outperform all state-of-the-art methods. This result shows that our method is not only suitable for the detection of dense scene text but also can achieve good results for the detection of curved and multi-oriented scene text.