## 3 Methodology
### Comprehensive Differentiating Feature Pyramid  
Inspired by other text detection and object detection methods [19; 20], mainstream approaches often concentrate on the deep interplay and fusion of interlayer features. Nevertheless, these methods tend to overlook the potential important regularities among intralayer features, which have been widely verified by earlier research and have made significant contributions to improving the accuracy of visual recognition tasks. To compensate for this deficiency while drawing inspiration from recent advances in dense prediction tasks [21; 22], this paper proposes an Integrated Differentiating Feature Pyramid (IDFP), whose specific structure is shown in Figure 3.  
In the text detection network pipeline, the input image is passed through an Integrated Differentiating Feature Pyramid (IDFP) backbone network to extract multi-scale features. Following this, the pyramidal features are upscaled to a uniform scale and fed into the Texture Fusion Module to generate a feature, F, rich in contextual information. The IDFP backbone network uses Swin Transformer V2 [23] as its base. After passing through the Patch Partition layer, which performs image partitioning, the raw image extracts a four-level feature pyramid, P1, P2, P3, and P4, with respective spatial sizes of 1/4, 1/8, 1/16, and 1/32 for the input image. Then, the P3 feature map is added pixel-wise to the correspondingly upscaled P4 feature map, resulting in P7, as described in Equation (1). \[\text{P7}=\text{P3}+\text{Up}_{\times 2}(\text{P4}) \tag{1}\]  
The pixel values corresponding to the P2 feature map, as well as those of the P7 feature map after upsampling two times and the P4 feature map after upsampling four times, are summed up to obtain P6, defined as follows:  
\[\text{P6}=\text{P2}+\text{Up}_{\times 2}(\text{P7})+\text{Up}_{\times 4}(\text{ P4}). \tag{2}\]  
The pixel values corresponding to the P1 feature map, along with those from the P6 feature map that has been subjected to 2\(\times\) upsampling and the P4 feature map that has undergone 8\(\times\) upsampling, are combined to produce the P5 feature map, described as follows:  
\[\text{P5}=\text{P1}+\text{Up}_{\times 2}(\text{P6})+\text{Up}_{\times 8}(\text{ P4}). \tag{3}\]  
The P5 feature map is processed through a 3 \(\times\) 3 convolutional layer. The P6 feature map undergoes a 3 \(\times\) 3 convolutional layer, followed by a 2\(\times\) upsampling operation. The P7 feature map is first passed through a 3 \(\times\) 3 convolutional layer and then subjected to 4\(\times\) upsampling. Similarly, the P4 feature map is processed via a 3 \(\times\) 3 convolutional  
Figure 3: Architecture of the IDFP module. layer before being upscaled by a factor of 8. Finally, these four feature maps, namely P5, P6, P7, and P4, are input into the texture aggregation module. This process is detailed in Equations (4)-(7). \[\text{P5}\prime=\text{Conv}_{3\times 3}(\text{P5}) \tag{4}\]  
\[\text{P6}\prime=\text{UP}_{\times 2}(\text{Conv}_{3\times 3}(\text{P6})) \tag{5}\]  
\[\text{P7}\prime=\text{UP}_{\times 4}(\text{Conv}_{3\times 3}(\text{P7})) \tag{6}\]  
\[\text{P4}\prime=\text{UP}_{\times 8}(\text{Conv}_{3\times 3}(\text{P4})) \tag{7}\]  
The design of IDFP further optimizes the traditional feature pyramid structure, ensuring the efficient fusion of shallow features while benefiting from the rich and concentrated visual information contained within the deepest layers. Considering that the deepest-layered features typically contain the most abstract representation, whereas the shallowest ones are scarce, this chapter leverages explicit visual center cues extracted from the deepest levels of the feature pyramid to synchronize and finely adjust related shallow features. This approach not only promotes communication between different hierarchical features but also significantly enhances the ability of shallow features to represent key visual elements in images.