# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 4 Experimental Results
### Experimental Results  
Five methods were tested on the ICDAR2013 and SVT datasets: FCN, SSD, SSD-OMC, SSTD, and SSTD-OMC. The SSD-OMC and the SSTD-OMC use the proposed algorithm to combine the semantic segmentation with SSD and SSTD, respectively.  
Table 3 shows the tested results of five methods on the ICDAR2013 dataset using two standard evaluation protocols. It can be seen from the test results that the SSD-OMC and SSTD-OMC algorithms showed an increase of 17.10% (IC13), 17.09% (DetEval) and 5.97% (IC13), 5.04% (DetEval) in the recall rate relative to the SSD and SSTD algorithms, which means that more texts missed by the multibox processing was detected. The SSD-OMC method was 9.60% (IC13) and 10.93% (DetEval) higher than the SSD method in the F-measure score, and the SSTD-OMC method was 2.55% (IC13) and 4.36% (DetEval) higher compared to the SSTD method in the F-measure score, meaning that the multibox processing optimized by our algorithm had a better detection accuracy.  
Table 4 shows the results of the SSTD-OMC algorithm and four other advanced text detection algorithms tested on the ICDAR2013 dataset. As can be seen in the table, SSTD-OMC has a higher f-measure score, indicating that SSTD-OMC had the better detection accuracy among these five algorithms. Some detection results are shown in Figure 10.  
Table 5 shows the results of five methods tested on the SVT dataset. The SSD-OMC method showed an improvement of 13.62% (IC13) and 12.78% (DetEval) on the F-measure score compared to the SSD method, while the SSTD-OMC method improved 1.94% (IC13) and 4.82% (DetEval) on the F-measure score compared to the SSTD method. Figure 11 shows some detection results from the SVT dataset.  
\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 Yin [34] & 0.66 & 0.88 & **0.76** & 0.69 & 0.89 & **0.78** \\ Neumann [35] & 0.72 & 0.82 & **0.77** & - & - & - \\ Zhang [36] & 0.74 & 0.88 & **0.80** & 0.76 & 0.88 & **0.82** \\ Textboxes [22] & 0.74 & 0.86 & **0.80** & 0.74 & 0.88 & **0.81** \\ SSTD-OMC & 0.80 & 0.82 & **0.81** & 0.80 & 0.80 & **0.83** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Improved algorithm compared with other advanced 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 1**  & **P 2**  & **F 3**  & **R** & **P** & **F** \\ \hline FCN & 62.54\% & 60.80\% & **61.66\%** & 66.97 & 62.05\% & **64.42\%** \\ SSD & 52.20\% & 87.76\% & **65.18\%** & 53.06\% & 87.24\% & **65.98\%** \\ SSD-OMC & 69.30\% & 81.21\% & **74.78\%** & 70.15\% & 85.10\% & **76.91\%** \\ SSTD & 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}
\end{table}
Table 3: Improved algorithm compared with original algorithm (on ICDAR2013).