# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 3 Proposed Methodology
### Text Aware Non-Maximal Suppression
Table 3 presents the summary of evaluation results which shows a consistent improvement in detection performance on test datasets with the incorporation of attention blocks in comparison with the baseline performance presented in Table 2. Table 3 also shows the performance of other state-of-the-art benchmarks. The best results for each measure are highlighted in bold. In addition, the next three best results are italicized. It is noteworthy that **MobileTDNet** achieves the best F-score on the _ICDAR2015_ and _COCOText_ datasets. Furthermore, our method has one of the top three F-scores for _ICDAR2013_ and one for top three recall rates for the _MSRA-TD500_ dataset. Our analysis also focuses on the IoU measure on the _MSRA-TD500_ dataset, which covers a wide range of variations in text appearances in a small collection of training and test sets. The overall performance of **MobileTDNet** on this dataset lags behind that of the _ICDAR2013_ and _COCOText_ datasets. To establish the contribution of attention blocks, the average IoU on the testing set is analyzed under different attention block settings, observing the average processing time to determine the average computational complexity. As shown in Table 5, the incorporation of attention blocks improved the average IoU measure on the testing set, although with a marginal difference. The average IoU improvement is reflected as an increase of 2.90% in the detection F-score in comparison with the baseline result presented in Table 2.

Figures 5 and 6 further establish the efficacy of the proposed scene text detection pipeline. Figure 5 shows the quadrilateral bounding box detections for an example input, measuring the average IoU of detected quadrilateral boxes. In this case, the incorporation of **Text Attention 1, 2 and 3** blocks improved the average IoU by 2.20% with respect to the baseline, i.e., without text attention. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **W/O Attention** & **Text Attention 1** &
\begin{tabular}{c} **Text Attention 1, 2** \\ **and 3** \\ \end{tabular} \\ \hline Avg. IoU & 0.772 & 0.786 & 0.793 \\ \hline Avg. processing time in seconds & 0.385 & 0.415 & 0.486 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of attention blocks on _MSRA-TD500_ detections using IoU measure. Again, Figure 6 shows the filtered text confidence scope map for another example input with/without the incorporation of text attention blocks. The incorporation of **Text Attention 2 and 3** significantly improves the text/non-text segmentation by removing spurious text segments. ## 5 Discussion

The **MobileTDNet** detection results in Table 3 present the best precision and F-score on the _COCOText_ dataset with the next best recall rate. It is remarkable as the _COCOText_ dataset is the largest of all datasets used in this work, where dataset images were collected for scene understanding research and consist of a large scale of diversity and variations in text appearances. Similarly, the proposed detector achieves the highest F-score for the _ICADR2015_ dataset with the third best precision score. **MobileTDNet** improved on many prominent methods, including TextBoxes++ [36], EAST [32] and PixelLink [46], as shown in Table 3. Concurrently, the **MobileTDNet** performance does not surpass the state of the art on the _ICDAR2013_ and _MSRA-TD500_ datasets, although it is positioned in the top-3 in precision and F-score for _ICDAR2013_ and in the recall rate for _MSRA-TD500_. The training set of these datasets is comparatively smaller, and it is likely that the **MobileTDNet** extracted features do not preserve sufficient representative attributes to learn an effective model. DB-ResNet-50 [72] performance closely matches with **MobileTDNet** on _ICDAR2015_ {precision, recall, F-score difference: 0.005, \(-\)0.015, \(-\)0.006}; however, it performs better on the _MSRA-TD500_ dataset {precision, recall, F-score difference: 0.061, \(-\)0.015, 0.019}.