# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 2 Literature Survey  
[PERSON] et al. [25] presented an early application of convolutional networks designed with convolution, sampling, and activation layers for text detection in images. Subsequently, [PERSON] et al. [26] demonstrated a convolutional network for scene text recognition combining text localization, character recognition, and lexicon-driven word recognition. On similar lines, [PERSON] et al. [27] demonstrated jointly trained convolutional networks with shared weights for text detection and recognition. However, these methods analyze the input by spatial scanning in the image space, which is computationally inefficient. With progress in convolutional neural network based object detectors, e.g., Fast RCNN [28], RFCN [29], SSD [30], and Mask RCNN [31], many recent scene text detection methods followed similar designs to directly predict boundary boxes around the text segments [32; 33; 34; 35; 36; 37]. The early deep convolutional neural network design for text detection focused on axis-aligned rectangular box prediction around text segments [38; 39; 40; 41; 42]. The method by [PERSON] et al. [40] presented a convolution RNN for text line proposal generation and merging. Similarly, [39] showed the application of residual features in a convolutional RNN framework for scene text detection. However, the textual content in natural images is never constrained within the rectangular boundary but appears in arbitrary orientations and curved shapes within complex backgrounds. [PERSON] et al. [43] proposed a text proposal network founded on the RPN design in the Faster RCNN object detector. The proposals are refined using an LSTM to generate polygonal bounding boxes around text segments. In [33], [PERSON] et al. designed a fully convolutional network for detecting the text saliency map in a given input, which is subsequently processed by the MSER for text detection. CharNet [44] presented a convolutional architecture combining the character and text instance detection in a single network. There are also methods that focus on the segmentation of text regions for the detection of irregular and complex text instances [34; 45; 46]. Mask Textspotter [34] applied the Fast RCNN detector with a convolutional branch for text and character-level image segmentation. PixelLink [46] presented a novel formulation for modeling image-level pixel associations using a convolutional architecture. [PERSON] et al. [47] and [PERSON] et al. [48] presented deep convolutional neural architectures by processing Mask RCNN generated text instances combined with feature pyramids. The direct regression of bounding boxes around text instances has been another research approach to solve scene text detection. Regarding the direct regression for multi-oriented scene texts, [PERSON] et al. [49], EAST [32], SegLink [50], and [PERSON] et al. [51] presented some prominent methods based on deep learning for regression. EAST presented a U-shaped convolutional architecture for the prediction of words or text lines, whereas SegLink presented a convolutional architecture for learning associations between text segments at multiple levels of feature maps. [PERSON] et al. [52] proposed a convolutional architecture network to regress the corner points around text segments, which are combined to generate quadrilateral text boxes. The LOMO detector [53] again pursued the regression of corner points around text segments with additional convolutional modules for the refinement of bounding box prediction and text shape learning. In addition, there are methods for solving the problem in a bottom-up manner following the character/text symbol detection followed by sequence analysis for word and text line detection [33; 42; 44; 54]. However, the majority of these methods have ignored the computational requirement for the proposed method, which is critical in many applications such as mobile and augmented reality. There are also some works on scene text detection for applications with limited computational support [55; 56; 57; 58].