# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 3 Proposed Methodology
### Text Aware Non-Maximal Suppression
For the dataset, the absence of the \(F^{\frac{1}{2}}\) feature map affects model learning more than the \(F^{\frac{1}{2}}\) feature map. On the contrary, for _ICDAR2013_, the \(F^{\frac{1}{2}}\) feature map is more important than that of \(F^{\frac{1}{2}}\). The possible reason could be that the _ICDAR2015_ consists of more variations including incidental text appearances with non-regular bounding boxes where skip connections capturing fine features are more important. In the next step, the visualization of detections for an example is investigated under two settings: (1) the network trained with \(\{F^{\frac{1}{4}}\), \(F^{\frac{1}{6}}\), \(F^{\frac{1}{6}}\), \(F^{\frac{1}{6}}\)} and (2) with \(\{F^{\frac{1}{8}}\), \(F^{\frac{1}{6}}\), \(F^{\frac{1}{6}}\)} skip connections. As shown in Figure 4, the removal of the \(\{F^{\frac{1}{4}}\}\) skip connection decreased the average IoU for the detections marginally. However, the careful observation of Figures 3f and 4a,b shows that detection quality significantly decreases with the removal of skip connections at higher spatial resolutions. This is also observed in the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a) ICDAR201**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \begin{tabular}{c} **Skip Connection: Feature** \\ **Maps** \\ \end{tabular} & **Pre.** & **Rec.** & **F-Score** & \begin{tabular}{c} **F-Score** \\ **Difference** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Detection** \\ **Time in** \\ **Seconds** \\ \end{tabular} \\ \hline \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.835 & 0.814 & 0.823 & _0.062_ & 0.278 \\ \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.787 & 0.763 & 0.775 & 0.110 & 0.265 \\ \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}}\}\) & 0.735 & 0.704 & 0.719 & 0.166 & 0.246 \\ \hline \(\{F^{\frac{1}{2}}\}\) & 0.705 & 0.678 & 0.691 & 0.194 & 0.231 \\ \hline \(\{F^{\frac{1}{4}},F^{\frac{1}{4}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.842 & 0.801 & 0.821 & _0.064_ & 0.267 \\ \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.777 & 0.751 & 0.764 & 0.121 & 0.280 \\ \hline \hline \multicolumn{5}{c}{**(b) ICDAR2015**} & \multicolumn{1}{c}{} \\ \hline \hline
\begin{tabular}{c} **Skip Connection: Feature** \\ **Maps** \\ \end{tabular} & **Pre.** & **Rec.** & **F-Score** & **F-Score Difference** \\ \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.825 & 0.788 & 0.806 & _0.047_ \\ \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.819 & 0.778 & 0.798 & 0.055 \\ \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}}\}\) & 0.684 & 0.627 & 0.654 & 0.199 \\ \hline \(\{F^{\frac{1}{2}}\}\) & 0.664 & 0.581 & 0.620 & 0.233 \\ \hline \(\{F^{\frac{1}{2}},F^{\frac{1}{4}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.829 & 0.796 & 0.812 & _0.041_ \\ \hline \(\{F^{\frac{1}{4}},F^{\frac{1}{4}},F^{\frac{1}{4}},F^{\frac{1}{4}}\}\) & 0.736 & 0.749 & 0.742 & 0.111 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of skip connections on _ICDAR2013_ and _ICDAR2015_ datasets. overall results presented in Table 4. The average timing analysis for _ICDAR2013_ shows that skip connections at lower spatial resolutions are relatively computationally intensive, as lower layers in the network contribute a bigger share of network weights. ### Incorporation of Text Attention Blocks

Next, the contribution of text attention blocks in the **MobileTDNet** scene text detector is evaluated. The effect of text attention blocks into the detector network is analyzed in two steps. First, the **Text Attention 1** block is incorporated in the **MobileTDNet** network, which is followed by the incorporation of **Text Attention 2** and **Text Attention 3** blocks.