# CommuSpotter: Scene Text Spotting with Multi-Task Communication
## 4 Experiments
### Ablation Study  
The experiments adopt percentage values of the end-to-end recognition F-scores on the ICDAR15 dataset with a strong lexicon. We train a model that only adopts the Extensive Representation (ER) mechanism from the baseline Mask TextSpotter v2 [19]. The ER improves the recognition performance from 82.1% to 82.5% in Table 2.  
To validate the effectiveness of communication between text detection and instance segmentation tasks, we add the Conversation Mechanism (CM) to them but without expertise from text semantic segmentation. The recognition results indicate that concurrent communication can improve performance from 82.1% to 83.7%. If equipped with the above ER, it can achieve 84.6%. Finally, with full settings of CM, we add the interaction from the text semantic segmentation. The F-score is further improved from 84.6% to 85.8%. Thus, there is a collaborative effect for the entire text spotter.  
We train and fine-tune several models of the configurations on the Total-Text dataset, as shown in Table 3. The ER improves the spotting performance from the baseline Mask TextSpotter v2 [19] of 77.4% to 78.4%. The singular CM-P can improve the performance from 77.4% to 80.3%. Together with ER, the recognition results indicate that concurrent communication can improve performance to 81.7%. With full settings of CM, we add the interaction from text semantic segmentation. The F-score is further improved to 83.4%.  
As mentioned above, some methods adopting Transformer or customized modules may train the model on extra datasets or make corresponding extra labels for the datasets. It causes difficulty in the fair comparison of different approaches. We compare the model efficiency in Table 4. For the abbreviation of datasets, \"CST\" is Curved SynthText [8]; \"COCO\" is COCO-Text [45]; \"MLT\" is ICDAR-MLT [46]; \"IC13\" is ICDAR2013 [41]; and \"CTW\" is SCUT-CTW1500 [47]. We compare the costs of computation resources with some approaches in Table 4. For the maximum iterations of convergence in the training process, we only need 360 K iterations in total, which is almost the fastest model. The GPU hour is roughly estimated from previous papers or re-implementation. Our GPU hour is a little more than the most  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Extensive Representation** & **CM-P** & **Conversation Mechanism** & **F-Score** \\ \hline E2E-baseline & & & 82.1 \\ w/ER & ✓ & & 82.5 \\ w/CM-P & & ✓ & 83.7 \\ w/CM-P & ✓ & ✓ & 84.6 \\ Full Setting & ✓ & ✓ & ✓ & 85.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: End-to-end recognition results of ICDAR15 on different model configurations. “CM-P” stands for part of the Conversation Mechanism without text semantic segmentation expertise.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Extensive Representation** & **CM-P** & **Conversation Mechanism** & **F-Score** \\ \hline E2E-baseline & & & 77.4 \\ w/ER & ✓ & & 78.4 \\ w/CM-P & & ✓ & 80.3 \\ w/CM-P & ✓ & ✓ & 81.7 \\ Full Setting & ✓ & ✓ & ✓ & 83.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: End-to-end recognition results of Total-Text on different model configurations. “CM-P” stands for part of the Conversation Mechanism without text semantic segmentation expertise.  
lightweight ABCNet [8], but our performance is better than that work. As a result, our method can achieve competitive performance at a lower cost compared to previous approaches. It is a good trade-off between model performance and computational efficiency.