# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 1 Introduction  
Text processing in images and videos is an important problem in many digital applications. With advancements in mobile and augmented reality applications, efficient scene text processing can significantly improve the overall experience in such applications. Unlike conventional optical character recognition, scene text processing applications deal with much larger variations in text appearances in terms of the orientations, shapes, scripts, and scales. The localization of text segments in input images forms the precursor for understanding the texts embedded in scene images. In this case, a detector localizes the text regions in the input images as rectangular or quadrilateral bounding boxes. Early research on scene text detection focused on developing handcrafted features for modeling text components in natural scenes [1; 2; 3; 4]. These methods exploited the conventional image processing tools including edge detection, connected component analysis, morphological operations, and neighborhood analysis for content modeling in images and videos [5; 6; 7]. Stroke width transform (SWT) by [PERSON] et al. [2] and maximally stable extremal regions (MSERs) by [PERSON] et al. [8] are two important methods in this body of research. The SWT presented steps for pixel-level character stroke width analysis for text instance segmentation in images, whereas the MSER for scene text detection applied contiguous image segment analysis at multiple levels. The concepts proposed in SWT and MSER were subsequently in many methods for scene text analysis [9; 10; 11; 12]. However, these methods fail to address the variations and complexities in real-life scene text detection due to the inherently parameter-sensitive design steps. With the success of deep neural networks-based image recognition [13], many recent methods have explored various designs of deep convolutional neural networks for scene text analysis [14; 15]. The majority of these methods focused on improving text detection performance by applying complex convolutional neural net architectures to enhance the representation power of detection models. However, this limits the applicability of developed methods in real-time applications because of computational requirements. In addition, despite significant effort, the existing convolutional architectures fail to handle complex scene text appearances in natural scenes as observed from the state of the art on public datasets [16; 17; 18]. To this end, the paper presents a novel scene text detector based on convolutional neural networks for predicting quadrilateral bounding boxes on scene text instances. In addition to the detection accuracy, our approach focuses on a computationally lightweight and accurate scene text detector suitable for digital applications with limited computational resources, e.g., mobile and embedded applications. The proposed detector uses the MobileNet model as the backbone, where the detector network is designed in a U-shaped encoder-decoder architecture by introducing skip connections for regulated feature fusion at multiple levels. The selection of MobileNet was made for its lightweight and efficient design based on the depthwise separable convolution operation [19]. The challenges of text detection in natural scenes bridge over to conventional object detection and object instance segmentation in images. Therefore, the proposed detector is trained in multitask learning, combining the text instance segmentation with quadrilateral bounding box prediction around text instances. The combination of dual objectives guides the detector to capture text attributes for arbitrarily oriented, complex shaped, and styled scene text detection. In this convention, the subsequent layers in sequential convolutional network design extract finer details from the input feature map by information aggregation using layerwise convolutional operation and feature sampling.