# Compact and Accurate Scene Text Detector
## 4 Experiment
### Discussion  
By the experimental results of Tables 1-3, we demonstrated that the balanced decoder (B) is efficient and effective. The balanced decoder has an IRB in its two stages, so one may argue that it can achieve better performance if it has an IRB in all stages. We compared the B with several different decoders through experiments with the same backbone MobileNetV2, and the results are summarized in Table 5, where the structures of IRB decoder 1 and IRB decoder 2 can be found in Figure 4. The EAST decoder (E) is the most efficient but is the worst in terms of the F1 score. The CRAFT decoder (C) is the largest and good effective, but has the worst FLOPS. IRB decoder 1 and IRB decoder 2 are similar to E and C, respectively, but they have IRBs at all stages. They commonly have comparable F1 scores, but are not impressive in terms of the efficiency (e.g., FLOPS). The reason is that adding an IRB to the last stage makes it more complex than the other stages, and such an imbalance between stages hinders finding appropriate word box candidates. On the other hand, the B has a balanced complexity between stages, so it has a better F1 score and FLOPS as well; if we compare B with E, then there is an F1 score gap of 2.5% between them.  
To determine if CAST works in light devices without a GPU, we performed additional experiments using only a CPU. The results are summarized in Table 6. Compared to CAST, the PixelLink and CRAFT are about 5\(\sim\)120 times worse in terms of the inference time and 31\(\sim\)49 times worse in terms of FLOPS. The CharNet is the most accurate but has an extremely poor efficiency; CharNet is a joint model of text detection and recognition, so detection results benefit from the recognition results but the model becomes extremely slow. Note that the inference time of CAST is at different unit; CAST is at the millisecond (ms) level, whereas the others are at the second (s) level. Such huge gaps in the inference time between CAST and the other models imply that the CAST has a relatively great potential for mobile environments.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **F** & **Time** & **FLOPS** & **Param** \\ \hline PixelLink [3] & 83.70 & 1.89 s & 650.2G & 20.5 M \\ CRAFT [2] & 86.90 & 42.66 s & 1,023.9G & 20.8 M \\ CharNet [34] & **90.97** & 1,230 s & 2,402.9G & 89.2 M \\ CAST & 81.06 & **352.90 ms** & **20.8G** & **2.8 M** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Inference time comparison using a CPU with the ICDAR2015 dataset, where \(F\) represents the F1 score. Here _ms_ and \(s\) represent milliseconds and seconds, respectively.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Dataset** & **F** & **Time** & **FLOPS** & **Param** \\ \hline CRAFT [2] & ICDAR13 & 1.18\(\times\) & 3.01\(\times\) & 53.68\(\times\) & 7.43\(\times\) \\ CharNet [34] & ICDAR15 & 1.12\(\times\) & 18.07\(\times\) & 115.52\(\times\) & 31.86\(\times\) \\ CharNet [34] & ICDAR17 & 1.19\(\times\) & 6.13\(\times\) & 30.59\(\times\) & 31.86\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison the CAST with the most accurate models for each dataset, where F indicates F1 score, and K\(\times\) means that the corresponding model is relatively \(K\) times better or worse than the CAST.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **R** & **P** & **F** & **FLOPS** & **Param** \\ \hline EAST decoder & 74.65 & 83.20 & 78.66 & **16.0 G** & 2.6 M \\ IRB decoder 1 & 75.20 & 84.75 & 79.69 & 21.1G & **2.6M** \\ IRB decoder 2 & 75.64 & 84.28 & 79.73 & 22.3G & 2.7 M \\ CRAFT decoder & 76.36 & 84.99 & 80.44 & 22.5G & 3.0 M \\ Balanced decoder & **76.79** & **85.84** & **81.06** & 20.8G & 2.8 M \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of several decoders with the ICDAR2015 dataset, where \(R\), \(P\), and \(F\) represent the recall, precision, and F1 score, respectively. The results are obtained using the same backbone MobileNetV2.