# CommuSpotter: Scene Text Spotting with Multi-Task Communication
## 4 Experiments
### Incidental Texts  
All the following comparison metrics on the text-spotting datasets are percentage values. We evaluate the method on incidental texts of IC15 [42]. The evaluation results are shown in Table 5. Our method achieves a state-of-the-art recall of 88.3% and an F-score of 89.8% compared with previous studies. For the end-to-end recognition evaluation, our method outperforms the previous non-Transformer methods in the strong and generic lexicon. The F-score achieves 85.8% and 74.9%. Compared with recent Transformer based methods, it is hard to distinguish the effects of the Transformer from the modulation. Also, TextTranSpotter (TTS) [15] achieves a higher F-score for the generic lexicon when trained with 43 K more images compared with our 4 K images. Our results demonstrate the effectiveness of communication among multi-tasks for end-to-end text spotting in incidental texts. Apart from the Transformer, this represents a promising exploration into the simple and clean modulation of text spotters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Pre-Train**} & \multicolumn{2}{c}{**Mix-Train or/and Fine-Tune**} & \multirow{2}{*}{**GPU**} \\ \cline{2-2} \cline{5-6}  & **Data** & **Iter.** & & **Data** & **Iter.** & \\ \hline MANGO [7] & SynthText & 600 K & CST, COCO, MLT, IC13, IC15, Total & 250 K & \(\sim\)1344 \\ ABCNet [8] & CST, COCO, MLT & 260 K & Total, CTW & 150 K & \(\sim\)288 \\ SwinTextSpotter [14] & CST, MLT, IC13, IC15, TT & 450 K & IC13, IC15, Total, MLT, CTW & 90 K & \(\sim\)336 \\ TTS [15] & SynthText & - & SynthText, IC13, IC15, Total, SCUT, COCO & - & \(\sim\)1200 \\ Mask TextSpotter v2 [19] & SynthText & 270 K & SynthText, IC13, IC15, Total, SCUT & 150 K & \(\sim\)432 \\ \hline Ours & SynthText & 270 K & IC13, IC15, Total, SCUT & 90 K & \(\sim\)312 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between training configuration and computation cost. In the pre-training, mix-training, and/or fine-tuning stages, the word “Data” represents the datasets used for training, and “Iter.” denotes the number of convergence iterations needed. “GPU” denotes the estimated GPU hours for each method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Detection**} & \multicolumn{2}{c}{**End-to-End**} \\ \cline{2-6}  & **P** & **R** & **F** & **S** & **W** & **G** \\ \hline FOTS [9] & 91.0 & 85.2 & 88.0 & 81.1 & 75.9 & 60.8 \\ [PERSON] et al. [5] & 89.4 & 85.8 & 87.5 & 83.4 & 79.9 & 67.9 \\ Mask TextSpotter v1 [13] & 91.6 & 81.0 & 86.0 & 79.3 & 73.0 & 62.4 \\ Text Perceptron [21] & 91.6 & 81.8 & 86.4 & 80.5 & 76.6 & 65.1 \\ TextDragon [28] & 92.5 & 83.8 & 87.9 & 82.5 & 78.3 & 65.2 \\ CharNet [25] & 91.2 & 88.3 & 89.7 & 80.1 & 74.5 & 62.2 \\ ABCNet v2 [26] & - & - & - & 82.7 & 78.5 & 73.0 \\ CRAFTS [23] & 89.0 & 85.3 & 87.1 & 83.1 & **82.1** & 74.9 \\ Boundary [27] & 89.8 & 87.5 & 88.6 & 79.7 & 75.2 & 64.1 \\ Mask TextSpotter v2 [19] & 86.6 & 87.3 & 87.0 & 83.0 & 77.7 & 73.5 \\ Mask TextSpotter v3 [6] & - & - & - & 83.3 & 78.1 & 74.2 \\ MANGO [7] & - & - & - & 81.8 & 78.9 & 67.3 \\ SwinTextSpotter [14] & - & - & - & 83.9 & 77.3 & 70.5 \\ TTS [15] & - & - & - & 85.2 & 81.7 & **77.4** \\ GLASS [48] & - & - & - & 84.7 & 80.1 & 76.3 \\ SRSTS [29] & **96.1** & 82.0 & 88.4 & 85.6 & 81.7 & 74.5 \\ \hline Ours & 91.4 & **88.3** & **89.8** & **85.8** & 80.2 & 74.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results on the ICDAR2015 dataset. For the detection result, “P”, “R”, and “F” represent the metrics of precision, recall, and F-score, respectively. The end-to-end recognition evaluation is the F-score. “S”, “W”, and “G” means strong, weak, and generic lexicons, respectively.