# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 1 Introduction  
Text reading in scene images is an important task driven by a set of real-world applications, such as image retrieval, license plate recognition, multi-language translation, etc. Scene text detection, followed by text recognition, is an essential procedure of visual text reading. However, due to the varieties of text patterns and complex backgrounds, scene text detection remains a challenging problem. Among the existing methods for scene text detection, there are mainly two prevalent types: detection-based [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11] and segmentation-based [12; 13] algorithms. The former methods, drawing inspiration from general object detection, design types of anchors to generate candidate regions and filter out false-positive regions to produce accurate bounding boxes for text instances. The latter algorithms, approaching text detection from a segmentation perspective, first adopt the fully convolutional network to perform semantic segmentation and then seek text localizations in segmentation maps. Essentially, to correctly detect the text in the scene image, the localization and boundary of each text instance should be accurately predicted. Detection-based methods usually work well in most cases. However, when there are large-scale variations and multi-oriented text, the anchor designing [1; 4; 9] and anchor matching [10; 11] might be complex and difficult. The semantic segmentation-based algorithms explore a different way and exhibit stronger adaptability to scale the variation and arbitrary shapes of text [12; 13]. However, it is difficult to successfully separate each text instance from the dense arrangement in thesegmentation map (Figure 1b). In this case, using post-processing, such as Non-Maximum Suppression (NMS)_, it would difficult to produce accurate boundaries for these text instances. The root cause of the above problem might be that the distinguishing operation of text instances relies on the binarization of the segmentation map. The vague segmentation result and sole binarization threshold might result in such embarrassments. In this paper, we propose a text detection framework to solve the above problem called AT-text (Assembling Text Components for dense scene text detection). AT-text improves the discrimination of the segmentation map and explores the bottom-up mechanism to alleviate the problem of spurious text instances. To cope with the interference of complex background, we combine the high-level semantic features and low-level appearance features of the segmentation model, achieving a more robust and better segmentation performance. Regarding the challenging of the implicit boundary of dense text instances, we decompose text regions into components for exploring local detail features. In these bottom-up methods, such as in Figure 1c, some generated components are difficult to deal with in post-processing. Therefore, they are generally built on whole-character component detection. We presume each component generally represents a small part of a word/text line, which may include part of a character, a single character, or multiple characters, etc. The segmentation result, such as the text mask in Figure 1b, allows the AT-text to efficiently filter out false-positive candidate components, making it powerful for assembling the remaining text components into different text instances. The contributions in this paper are summarized as follows:  
1. We propose a novel dense text detection framework in the bottom-up mechanism, without character-level annotation, to generate parsimonious text components and assemble them into word/text lines. This endows the framework with the flexible ability to make use of local information and multi-language text detection. 2. We employ a segmentation model that encodes multi-scale text features, considerably improving the classification accuracy of text candidate components. This allows the AT-text to efficiently filter out false-positive components, making it powerful in dealing with densely arranged text instances. 3.