# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 3 Proposed Methodology
### Text Aware Non-Maximal Suppression
DB-ResNet-50 [72] and Mask TextSpotter [34] detect polygonal text segments in images and

Figure 5: **MobileTDNet** text detection for an example: (**a**) shows detection without text attention blocks, average IoU = 0.859; (**b**) shows detection with **Text Attention 1** block, average IoU = 0.873; (**c**) shows detection with **Text Attention 1, 2 and 3** blocks, average IoU = 0.881. Figure 6: Filtered text confidence score maps for the example in (**a**): the score map with **Text Attention 1** block shown in (**b**), and with **Text Attention 1, 2 and 3** blocks shown in (**c**). require more complex processing in the convolutional network architecture. In contrast, the proposed method presents a simple convolutional architecture for scene text detection using depthwise separable convolutions, where the detector is designed for quadrilateral text box prediction in real-time applications. Our method also achieves performance on par with SPCNET [47]. On _ICDAR2013_, **MobileTDNet** improves precision by 0.009 but trails on recall and F-score by 0.029 on recall and 0.011. Again, on _MSRA-TD500_, our method improves precision and F-score by 0.026 and 0.007 but trails in recall rate by 0.011. The SPCNET [47] architecture is designed for multi-scale feature extraction using a feature pyramid network (FPN) [75]. **MobileTDNet** also improves on precision and F-score measures on _ICDAR2015_ in comparison with OPMP [73], which is also based on FPN-based multi-scale feature analysis. Our method also improves the recall rate on _MSRA-TD500_ in comparison with OPMP by 0.027. The FPN is widely regarded as a state-of-the-art feature extractor based on deep convolutional networks with conventional convolution operation, with the goal to extract high-quality representative features. The proposed detector is designed for accurate scene text detection in images with all possible variations in text appearance, orientation, and background, and it has computationally efficient scene text detection suitable for real-time applications. With **MobileTDNet**, we achieve both objectives as a highly accurate scene text detector designed on depthwise separable convolution based deep convolutional network. Nevertheless, the direct prediction of quadrilateral bounding boxes is not accurate in the case of complex curved shapes in text appearances, which requires further investigation to split and merge bounding box predictions. ## 6 Conclusions and Future Work

The paper presented a novel convolutional net architecture for scene text detection, which can detect arbitrarily shaped text segments in scene images predicting quadrilateral bounding boxes with high accuracy. Our work demonstrated a novel application of a depthwise separable convolutions-based MobileNet in a U-shaped network design for a scene text detector with a text attention mechanism. The extensive experiments on standard public datasets establish the efficacy of the presented methodology in comparison with the state of the art where the results demonstrate that **MobileTDNet** outperformed many prominent scene text detection methods. **MobileTDNet** is designed for mobile and embedded applications as the target using the computationally efficient depthwise separable convolution operation. The incorporation of split and merge strategies into the proposed detector is the next problem to be explored in the proposed methodology. It can address the detection of complex shaped curved text instances, which is an important limitation of the present method. In addition, extending the proposed method into an end-to-end scene text recognition pipeline is another direction for future work. Investigation, L.V.L. ; Project administration, E.H.; Supervision, E.H.; Writing--original draft, E.H. All authors have read and agreed to the published version of the manuscript. The project was fully funded by the Kuwait Foundation for the Advancement of Sciences under the Project Grant PR19-18 QI-01. The datasets used in the presented study are openly available at [65; 66; 67; 69].