# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 2 Related Works  
In the past few decades, scene text detection has attracted a large amount of attention and numerous works have been reported [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. These works have been dominated by bottom-up methods, which are generally built on character component detection. They can be roughly divided into two main groups: sliding window-based methods [1; 4; 12; 13] and component-based methods [2; 3; 5; 6; 7; 8; 9; 14; 15; 17; 19; 20; 24; 25; 26]. Sliding window-based methods usually apply some specially designed features to exhaustively search the location of text in raw images [12]. For the sake of the diversity of text patterns, sliding windows or raw images usually adopt a multi-scale strategy to match the text, leading to a large computational burden. Subsequently, manual rules or well-trained classifiers were employed to ensure that the remaining sliding windows contain text. This is good for narrowing down the number of sliding windows. However, manually designed rules inherently limit the capability of handling multi-language and complex backgrounds efficiently. [PERSON] et al. [23] ran a \(32\times 32\) pixel-window over the input images to obtain candidate text lines and then estimated the text locations in the candidate lines. [PERSON] et al. [1] extracted the symmetry features and appearance features of text with a template consisting of four rectangles to detect symmetry axis as text line candidates. [PERSON] et al. [4] computed a text saliency map based on a sliding window, followed by a character/background classifier. Traditionally, component-based methods commonly contain four stages: component extraction, component analysis, candidate linking, and chain analysis [2]. The first stage usually generates more candidate components by clustering algorithms. The candidate linking stage assembles the character candidates into a pair based on similar geometric and color properties, and then aggregates the pairs into chains. The two analysis stages both employ trained classifiers to verify the components and chains sent from the former stages. Their main difficulties lie in utilizing the discriminative features to train the high-capability classifiers. Therefore, the methods in this group make an effort to extract the discriminative features of text regions. SWT (Stroke Width Transform) [24] and MSERs (Maximally Stable Extremal Regions) [27] are two representative component-based methods. They extract characters with stroke width or intensity contrast properties. The SWT-based method [2] heavily depends on a single threshold, stroke width, which limits the performance of the detection in varieties of text patterns. [PERSON] et al. [25] extracted the strokes of characters based on intensity, spatial, and color-space constraints. [PERSON] et al. [26] traversed along horizontal scan lines and found that horizontal text lines had a similar vertical stroke width. [PERSON] et al. [24] densely voted on each stroke pixel to obtain a stable identification of strokes and grouped candidates without a brittle process in the other works. MSERs-based methods [5; 6; 7; 8; 9; 14; 17; 18; 20] progressively calculate intensity contrast by increasing the threshold to control the boundary of connected regions, and hence it is convenient for generating connected components. Due to great improvement of the classification task with Convolutional Neural Network (CNN) [28; 29], the deep convolutional neural network is exploited to distinguish text components with a high discriminant ability and strong robustness against a cluttered background [8; 9; 10; 11; 12; 13]. To generate more components for high recall, most MSERs-based methods usually apply two strategies. One of these is to preprocess the source images so that the algorithms can function in different variations of data--e.g., multi-channel or multi-resolution data. The other is that the parameters were changed to control the performance of the algorithm [8].