# Compact and Accurate Scene Text Detector
## 4 Experiment
### Results  
We compared CAST with several state-of-the-art models through experiments with three datasets (ICDAR2013, ICDAR2015, and ICDAR2017 MLT). In Tables 1-3, the M + B denotes the CAST, where M and B indicate the MobileNetV2 as a backbone and the balanced decoder, respectively. It is obvious that the MobileNetV2-based models (M + E, M + C, and M + B) achieve better efficiency than the other models. Especially, by comparing M + E and P + E, we can see the advantages of using the MobileNetV2 as a backbone, where the P + E (EAST) is the Pvanet version, not the Pvanet(2x) version. This implies that the backbone has a tremendous impact on the efficiency, so it is necessary to choose the backbone carefully. Regarding the effectiveness, the CAST achieves about 80\(\sim\)81% for the F1 score for ICDAR2013 and ICDAR2015, and 63% for the F1 score for ICDAR2017 MLT. For the ICDAR2013 dataset, Seglink has about a 5% greater F1 score than CAST, however Seglink has about a 6% smaller F1 score than CAST for the ICDAR2015. The reason is that Seglink is an SSD-based approach, so benefits from the relatively large word boxes of ICDAR2013. We compare CAST with the most accurate models  
Figure 5: The process of ground truth generation. (**a**) Score map generation by shrinking the word boxes. (**b**) RBOX generation based on the distance from the given pixel to the four edges. for each dataset, as summarized in Table 4. Compared to CAST, the most accurate models achieved about a 1.1 times better F1 score, but have long inference times of about 7\(\sim\)31 times and a larger FLOPS about 30\(\sim\)115 times. Based on this, CAST can be seen as significantly more efficient than the other models without losing much effectiveness. The MobileNetV2-based models, denoted as M+ models (M + E, M + C, and M + B), generally achieved better efficiency, but exhibit different performances for different datasets. When we look at the inference time for the ICDAR2013 dataset, the M+ models are slightly under performing. The ICDAR2013 dataset has relatively larger word boxes than the other datasets, which result in a spends longer time for post-processing. We also see that with the ICDAR2015 and ICDAR2017 MLT datasets, the inference time of CAST (M + B) is worse than M + E and M + C. The reason might be that the depthwise convolution of the IRB is known to need fragmented memory access, as described in [33]. Such phenomenon does not seem severe with the ICDAR2013 dataset; this is because the resized images are of relatively smaller resolution, leading it to suffer less from fragment memory access. The balanced decoder B is basically a stack of IRBs and standard convolutional layers. When we compare M \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **R** & **P** & **F** & **Size** & **Time** & **FLOPS** & **Param** \\ \hline P + E (EAST) [12] * & 66.57 & 90.27 & 76.63 & 512 (short) & 63.3 ms & 5.2 G & 3.2 M \\ PixelLink [3] & 83.60 & 86.40 & 84.50 & 512 \(\times\) 512 & 207.69 ms & 175.5 G & 20.5 M \\ Seglink [10] & 83.00 & 87.70 & 85.30 & 512 \(\times\) 512 & _50 ms_ & - & - \\ Mask TextSpotter [20] & 88.27 & 95.01 & 91.52 & 1000 (short) & _217.4 ms_ & - & - \\ SPCNET [21] & 90.59 & 93.77 & 92.16 & 848 (short) & - & 470.1 G & 35.5 M \\ CRAFT [2] & **92.40** & **97.67** & **94.96** & 960 (long) & 160.73 ms & 252.3 G & 20.8 M \\ \hline M + E & 69.57 & 91.38 & 78.99 & 512 (long) & **51.9 ms** & **3.6 G** & **2.6 M** \\ M + C & **69.97** & 91.10 & 79.15 & 512 (long) & 56.6 ms & 5.1 G & 3.0 M \\ M + B (CAST) & 69.69 & **94.78** & **80.32** & 512 (long) & 53.3 ms & 4.7 G & 2.8 M \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results with the ICDAR2013 dataset, where \(R\), \(P\), and \(F\) represent the recall, precision, and F1 score, respectively. An asterisk(*) indicates that the corresponding result is obtained in our own experiment. M and P denote MobilNetV2 and Pvanet, respectively. E, C, and B are the EAST decoder, the CRAFT decoder, and our proposed balanced decoder, respectively.