## 2 Related Work  
The primary task of scene text erasing involves receiving an image containing text and producing an output image devoid of any text. During this process, the network repairs the textual regions within the image using non-textual textures, aiming to achieve visually plausible effects. Image retrieval follows as a subsequent operation to text erasing; the more cleanly the text areas are erased and the more accurately the textures are repaired, the better the performance of the image retrieval task is enhanced. Therefore, how to precisely detect and repair text regions becomes the key issue in scene text erasing. Current research strategies can be categorized into two main types: first, single-stage methods based on image reconstruction; secondly, two-stage methods involving both text detection and image restoration. Single-stage methods were prevalent before the development of deep learning techniques. [PERSON] et al. [7] employed Gaussian filters, a simple image processing technology, for blurring purposes, but this method was effective only for texts with specific shapes. [PERSON] et al. [8] utilized a straightforward repairing algorithm that used sample precedents for restoration, reconstructing target text after filling in missing pixels. With the advancement of deep learning technologies, convolutional neural networks have demonstrated strong feature representation capabilities. [PERSON] et al. [9] were among the first to introduce the concept of SceneTextEraser for natural scene text erasing, utilizing a deep convolutional neural network to automatically detect, erase, and fill in natural scene text images. This method uses sliding windows to divide the image into multiple blocks and then employs U-Net to perform text erasing on each block and reassembles them to obtain the erased image. However, this method did not consider the relationships between image blocks, severely limiting the model's ability to capture contextual information, affecting the detection of text regions, and resulting in a lack of continuity and consistency in the erasure outcomes. [PERSON] et al. [2] introduced soft attention mechanisms to reduce the adverse effects caused by cropped text regions. They adopted hard attention methods to achieve more precise identification of image text information and incorporated a text-erasing network to enhance the feature extraction capability of convolution modules in text regions. Figure 1: Comparison of scene text and trademark text. [PERSON] et al. [10] presented EraseNet, dividing the text erasing process into coarse erasing and refinement stages. In the coarse erasing stage, the backbone network extracts features and provides them to the text perception branch and erasing branch. The text perception branch generates a text mask through features, comparing it with annotated masks to obtain mask loss. The erasing branch performs feature reconstruction as input for the refinement stage. In the refinement stage, the outputs from the erasing branch receive secondary erasure, and the result is compared with manually labeled erasures to identify the reconstruction loss. At the same time, the result is provided to an adversarial network to identify the adversarial loss. Although the model ensures more complete text erasure by performing secondary erasure on the image, the text mask generated via the text perception branch is not used in the next stage along with other branches, leading to issues such as erroneous erasure in EraseNet. Two-stage methods initially employ a text detection model to identify the location of text within the image or directly use scene text position annotations. Subsequently, they apply repairs to the detected text regions, completing the background where the text was present. MTRNet [11] utilizes text masks to provide positional information about the text. After the original image and the text mask are concatenated channel-wise, these inputs are fed into a generative network to produce the erased image.