# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 4 Experimental Results
### Exploration Study  
Inside feature extraction (IFE) and outside mutual correction (OMC) algorithms were tested. Since the IFE in SSTD cannot be removed alone, the performance without IFE cannot be tested. Therefore, only IFE and OMC algorithms were tested. The training and testing environments were consistent. All methods used the same training datasets, the same number of training epochs, and the same set parameters. We used two standard evaluation protocols: the IC13 standard and the DetEval standard [33]. The proposed method was implemented with Caffe and Matlab, running on a computer with an 8-core CPU, 32G RAM, TitianXP GPU, and Ubuntu 16.04. The complete test results are shown in Tables 1 and 2.  
'SSD' refers to the original SSD algorithm without IFE and OMC. 'SSD-IFE' refers to the SSD added IFE algorithm. 'SSD-OMC' refers to the SSD added OMC algorithm. 'SSTD-IFE' refers to the SSTD added IFE algorithm, which was the original SSTD. 'SSTD-OMC' refers to the SSTD added OMC algorithm.  
The experimental results show that the IFE algorithm reduces the accuracy of text detection on the ICDAR2013 dataset. For the SVT dataset, the IFE algorithm slightly improved the accuracy of the text detection. Compared to the IFE algorithm, the OMC algorithm significantly improved the F-measure score.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{3}{c}{**IC13 Standard**} & \multicolumn{3}{c}{**DetEval Standard**} \\ \cline{2-7}  & **R**\({}^{\mathbf{1}}\) & **P**\({}^{\mathbf{2}}\) & **F**\({}^{\mathbf{3}}\) & **R** & **P** & **F** \\ \hline SSD & 52.20\% & 87.76\% & **65.18\%** & 53.06\% & 87.24\% & **65.98\%** \\ SSD-IFE & 49.17\% & 82.79\% & **61.69\%** & 49.88\% & 83.29\% & **62.39\%** \\ SSD-OMC & 69.30\% & 81.21\% & **74.78\%** & 70.15\% & 85.10\% & **76.91\%** \\ SSTD-IFE & 74.54\% & 83.65\% & **78.83\%** & 75.39\% & 84.07\% & **79.50\%** \\ SSTD-OMC & 80.51\% & 82.27\% & **81.38\%** & 80.43\% & 87.60\% & **83.86\%** \\ \hline \hline \end{tabular} \({}^{1}\) Recall. \({}^{2}\) Precision. \({}^{3}\) F-measure.  
\end{table}
Table 1: Comparison of the IFE and outside mutual correction (OMC) algorithms (on ICDAR2013).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{3}{c}{**IC13 Standard**} & \multicolumn{3}{c}{**DetEval Standard**} \\ \cline{2-7}  & **R** & **P** & **F** & **R** & **P** & **F** \\ \hline SSD & 48.61\% & 73.24\% & **58.43\%** & 48.17\% & 75.12\% & **58.70\%** \\ SSD-IFE & 50.34\% & 79.91\% & **61.77\%** & 50.34\% & 79.91\% & **61.77\%** \\ SSD-OMC & 69.91\% & 73.57\% & **71.69\%** & 66.01\% & 77.97\% & **71.48\%** \\ SSTD-IFE & 78.60\% & 73.35\% & **75.89\%** & 78.60\% & 73.35\% & **75.89\%** \\ SSTD-OMC & 85.13\% & 71.68\% & **77.83\%** & 81.65\% & 79.78\% & **80.71\%** \\ \hline \hline \end{tabular} \({}^{1}\) Recall. \({}^{2}\) Precision. \({}^{3}\) F-measure.  
\end{table}
Table 2: Comparison of IFE and OMC algorithms (on SVT).  
Figure 9: Examples of the Street View Text (SVT) dataset. Text with lower resolution, complex light conversion, and smaller text.