# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 3 Proposed Methodology
### Text Aware Non-Maximal Suppression
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{_ICDAR2013 Dataset_} & \multicolumn{3}{c}{_ICDAR2015 Dataset_} & \multicolumn{3}{c}{_MSRA-TD500 Dataset_} & \multicolumn{3}{c}{_COCOText Dataset_} \\ \hline
**Method** & **Pre.** & **Rec.** & \begin{tabular}{c} **F-** \\ **Score** \\ \end{tabular} & **Pre.** & **Rec.** & \begin{tabular}{c} **F-** \\ **Score** \\ \end{tabular} & **Pre.** & **Rec.** & \
\begin{tabular}{c} **F-** \\ **Score** \\ \end{tabular} & **Pre.** & **Rec.** &
\begin{tabular}{c} **F-** \\ **Score** \\ \end{tabular} \\ \hline CTPN [40] & 0.930 & 0.830 & 0.877 & 0.740 & 0.520 & 0.610 & - & - & - & - & - \\ \hline Text-block & 0.880 & 0.780 & 0.830 & 0.710 & 0.430 & 0.540 & 0.830 & 0.670 & 0.740 & - & - & - \\ \hline RTN [39] & _0.940_ & _0.890_ & _0.910_ & - & - & - & - & - & - & - & - \\ \hline Lyu et al. [51] & 0.920 & 0.840 & 0.878 & _0.895_ & 0.800 & 0.844 & _0.880_ & 0.760 & 0.815 & _0.620_ & 0.320 & _0.425_ \\ \hline ATRR [43] & _0.937_ & _0.897_ & _0.917_ & 0.892 & _0.860_ & _0.876_ & 0.852 & _0.821_ & _0.836_ & - & - & - \\ \hline Mask TextSpotter [34] & **0.950** & _0.886_ & _0.917_ & _0.916_ & 0.810 & 0.860 & - & - & - & - & - & - \\ \hline Text-CNN [35] & 0.930 & 0.730 & 0.820 & - & - & - & 0.760 & 0.610 & 0.690 & - & - & - \\ \hline TextBoxes++ [36] & 0.910 & 0.840 & 0.880 & 0.878 & 0.785 & 0.829 & & & _0.609_ & _0.567_ & _0.587_ \\ \hline PixelLink [46] & 0.886 & 0.875 & _0.881_ & 0.855 & 0.820 & 0.837 & 0.830 & 0.732 & 0.778 & - & - & - \\ \hline DB-ResNet- & - & - & - & **0.918** & 0.832 & _0.873_ & **0.915** & _0.792_ & **0.849** & - & - & - \\ \hline EAST [32] & - & - & - & 0.833 & 0.783 & 0.807 & _0.873_ & 0.674 & 0.761 & 0.504 & _0.324_ & 0.395 \\ \hline Deng et al. [52] & - & - & - & - & - & - & - & - & - & _0.555_ & **0.633** & _0.591_ \\ \hline SPCNET [47] & 0.938 & **0.905** & **0.921** & 0.887 & _0.858_ & 0.872 & - & - & - & - & - & - \\ \hline LOMO [53] & - & - & - & 0.878 & **0.876** & _0.877_ & - & - & - & - & - & - \\ \hline OPMP [73] & - & - & - & 0.891 & _0.855_ & _0.873_ & _0.860_ & **0.834** & _0.847_ & - & - & - \\ \hline TextMountain [74] & - & - & - & 0.885 & 0.842 & 0.863 & - & - & - & - & - & - \\ \hline
**MobileTDNet +** & 0.941 & 0.853 & 0.895 & 0.903 & 0.842 & 0.871 & 0.843 & 0.802 & 0.822 & 0.627 & 0.596 & 0.611 \\
**Text Attention 1** & _0.947_ & 0.876 & _0.910_ & _0.913_ & 0.847 & **0.879** & 0.854 & _0.807_ & _0.830_ & **0.631** & _0.603_ & **0.617** \\
**2 and 3** & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MobileTDNet detection results with attention blocks: comparison with recent methods. Figure 3: MobileTDNet output for sample difficult cases displayed in subfigures (**a–f**). ### Effect of Skip Connections

The incorporation of skip connections is an important design component in the **MobileTDNet** architecture. The skip connections generate multiple levels of intermediate feature maps, i.e., \(F^{\frac{1}{2}}\), \(F^{\frac{1}{4}}\), \(F^{\frac{1}{8}}\), \(F^{\frac{1}{6}}\) and \(F^{\frac{1}{2}}\), which are subsequently processed to generate predictions from the network. To analyze the contribution by skip connections, the ablation experiments are performed by gradually removing skip connections in the **MobileTDNet** architecture. The experiments focus on the _ICDAR2013_ and _ICDAR2015_ datasets where the former dataset is dominated by horizontal text appearances. Table 4 shows the summary of experiments also having the F-score difference with the baseline results given in Table 2.

As observed in the results, all skip connections contribute almost equally in the learning task since the text appearances in input images can be in many forms, shapes, and styles. Concurrently looking at F-score differences: for _ICDAR2015_, the skip connections with intermediate features captured at higher spatial resolutions are marginally less effective than connections to carry features captured at lower spatial resolutions.