# Twin-FPN++: Rwin Transformer with Feature Pyramid Network for Dense Scene Text Spotting
## 2 Related Work  
We will describe the related work from three aspects, including scene text detection, scene text recognition, and scene text spotting.  
**Scene Text Detection.** In the past few years, great progress has been achieved for deep learning-based scene text detection. [PERSON] et al. [6] adopted fully convolutional networks (FCN) [7] to segment the text regions and predict their contours. [PERSON] et al. [8] modified feature pyramid networks (FPN) [9] to predict the segmentation results for each scene text region, and then used the PSE algorithm to compute the final detection result. [PERSON] et al. [16] modified the SSD [17] algorithm to detect arbitrary-shaped scene text. [PERSON] et al. [18] detected text regions in the Fourier domain and proposed a Fourier Contour Embedding (FCE) method to represent curved text contours. [PERSON] et al. [19] proposed a progressive contour regression approach to detect various aspect ratios of scene texts. Recently, Transformer has been adapted for computer vision and achieved state-of-the-art performances in image classification [12; 13] and object detection [13]. [PERSON] et al. [20] proposed  
Figure 1: Our spotting results compared with state-of-the-art approaches for the dense scene text image. (**a**) is the input image. (**b**) is the result of Pan++ [10]. (**c**) is the result of ABCNet v2 [5]. (**d**) is the result of our Rwin-FPN++ network.  
a simple and effective Transformer-based scene text detection network, which is mainly composed of a feature sampling module and a feature combination module. However, if Transformer is directly used to encode the tokenized image patches for segmentation and detection tasks, the results are usually unsatisfactory [14]. This is due to the fact that Transformer cannot extract local low-level visual cues well, which can be compensated by the CNN architectures (e.g., U-Net [15] and FPN [9]).  
**Scene Text Recognition.** The task of scene text recognition is to identify the text content of the segmented scene text area. [PERSON] et al. [21] adopted RNN to extract visual feature sequences produced by the CNN and achieved highly competitive performances on scene text recognition. [PERSON] et al. [22] recognized the low-quality scene texts robustly using an enhanced encoder-decoder network. [PERSON] et al. [23] recognized the scene text by extending the contrastive learning methods. [PERSON] et al. [24] proposed a bidirectional and autonomous ABINet to recognize the scene text.  
**Scene Text Spotting.** The purpose of scene text spotting is to detect and recognize scene text in a unified network. [PERSON] et al. [1] first adopted the CNN to detect and recognize the scene text. [PERSON] et al. [16] modified the SSD [17] algorithm to detect arbitrary-shaped scene text. [PERSON] et al. [2] employed Faster-RCNN [3] to detect scene text and a CTC loss [4] to recognize text. [PERSON] et al. [25] proposed a fully convolutional point-gathering network (PGNet) to recognize multi-oriented text instances in real-time. [PERSON] et al. [10] treated the text line as the text kernel and proposed an end-to-end network for curved text spotting. [PERSON] et al. [5] proposed an adaptive bezier-curve network to detect and recognize arbitrary-shaped scene text in real-time. However, these methods mainly focus on the spotting of arbitrary-shaped scene text, which is difficult to achieve satisfactory results on dense scene text containing various instances of bending, occlusion, and lighting.