## 2 Related Work  
For detecting text regions in natural scenes, existing methods can be broadly classified into two categories: methods based on text features and methods based on deep learning. Methods based on text features can be further divided into those based on connected component analysis and those based on sliding windows. Methods based on connected component analysis tend to detect text regions by designing feature extraction algorithms, among which the stroke width transformation (SWT) [3] and maximum stable extremal regions (MSER) [4] algorithms are representative. Methods based on sliding windows [5; 6; 7] slide a window over the original image, use a classifier to determine whether the window contains text, and finally post-process using non-maximum suppression. For example, [PERSON] et al. [5] use six different categories of text features to build weak classifiers, following which they construct a strong classifier from a combination of weak classifiers. Although detection methods based on text features have achieved a certain amount of success, their results are not satisfactory when facing complex natural scenes. The background of natural scenes is complex, containing vehicles, buildings, trees, pedestrians, and other noises that can interfere with detection. The fonts, sizes, and colors of text in natural scenes also vary. Finally, images of natural scenes are affected by the shooting environment, and there may be problems with different resolutions and blurred images caused by noise. In the face of these challenges, deep-learning-based methods have become the mainstream method and area of research for natural scene text detection. The models designed by deep-learning-based methods can be further divided into two categories: region proposal methods and semantic segmentation methods. Region proposal methods are usually built on classical target detection algorithms, such as Region-Based Convolutional Neural Networks (R-CNN) [8], Faster R-CNN [9], Single Shot MultiBox Detector (SSD) [10], and You Only Look Once (YOLO) [11]. [PERSON] et al. [12] proposed a network structure of TextBoxes based on the SSD model to detect horizontal text regions by setting six candidate bounding boxes with different aspect ratios. Later, [PERSON] et al. [13] proposed the TextBoxes++ model based on TextBoxes,which can detect text regions in any direction by adding angle information to the predicted bounding boxes. [PERSON] et al. [14] proposed the Connectionist Text Proposal Network (CTPN) model based on Faster R-CNN, which treats text regions as sequential information and introduces CNNs for processing. CTPN is better for long text region detection, but the introduction of CNNs greatly increases the number of parameters of the model, and the model is not effective in detecting text regions with high tilt angles. [PERSON] et al. [15] proposed the SegLink model based on CTPN, which divides text region detection into two parts: detection and linking. SegLink can link eight directions during linking, which resolves CTPN's issues with detecting high-tilt-angle text regions, but is also susceptible to detecting separate pieces as a whole for denser text regions. Semantic segmentation approaches [16; 17; 18; 19] often draw on classical semantic segmentation algorithms, such as Fully Convolutional Networks (FCN) [20] and Feature Pyramid Networks for Object Detection (FPN) [21], which use deep neural networks to extract multi-layer feature maps, perform multi-level feature fusion to obtain a feature map containing rich information, and then predict target segmentation results based on the fused feature maps. The TextField model, proposed by [PERSON] et al. [19], uses directional fields to represent the features of text regions to detect irregularly shaped text. [PERSON] et al. [22] argued that character-level detection is needed in scenarios such as mathematical formulas, but current datasets are text line-level or word-level annotations.