## 1 Introduction  
Compared to natural images, trademarks typically contain less information due to their strongly stylized characteristics and lack of rich textures commonly found in natural imagery. Moreover, trademarks may share common design elements such as characters and icons. The definition of trademark similarity is ambiguous and broad, encompassing aspects such as shape, sound, semantics, layout, texture, and local details. Recent studies [1, 2] have employed keypoint features like SIFT, deep-crafted DCNN features, and neural networks to address trademark retrieval. Although these advancements have significantly improved retrieval performance over previous methods, even the best-performing retrievals still fall short of acceptable levels of accuracy. The analysis of failed case studies for these systems indicatesthat poor performance is often attributed to similarities caused by text, background, minor symbols, contrast, or noise. Common failure cases relate to the textual elements within trademarks; textual elements can exhibit similarities yet not be considered infringing. There can also be similarities between graphic elements and textual elements; textual elements with sharp angles, holes, and strokes can produce features similar to those of graphical elements. Given the importance of visual salience in similarity detection [3], reducing the contribution of textual element features in calculating similarity values should promote the effectiveness of existing trademark retrieval methods. The integration of text erasure technology with image restoration techniques involves first precisely detecting the text regions within an image, followed by the efficient erasing of these areas, and then using image restoration to recover the filled background information, maintaining the overall naturalness and integrity of the image. However, text erasure tasks are more challenging than image restoration because they require the meticulous treatment of text regions to ensure effective erasure and background recovery while simultaneously being vigilant about non-textual regions to prevent erroneous modification or destruction during the process. Within the field of text erasure technologies, scene text erasure techniques dominate, primarily due to the ubiquity and variety of scene text images in everyday life. Scene text images, as omnipresent visual elements, enrich our visual experiences but also pose unprecedented challenges for text erasure technology. First, the backgrounds of scene text images are extremely complex, potentially including natural landscapes, urban architecture, indoor environments, etc., with diverse colors, textures, and brightness levels, posing difficulties for text region detection. Second, the variability of lighting conditions presents a major challenge, as different intensities, directions, and colors of light affect the visibility and recognition of text. Third, the layouts of text in scene text images are often free-form and variable, ranging from horizontal, vertical, and oblique to curved, and they require high flexibility and adaptability in text erasure algorithms. Lastly, the multitude of font types adds complexity to the task, from common print fonts to handwriting and artistic scripts, each with unique characteristics and identification hurdles. Trademark images, although characterized by specific design elements and styles, also face similar challenges in text erasure tasks. The elements within trademarks are diverse, extending beyond single backgrounds and possibly including patterns, colors, and other elements. The directionality and typography of trademark text can also vary arbitrarily. Therefore, the challenges faced by scene text erasure technology exist in trademark text erasure tasks as well, and they may even be more complex. As depicted in Figure 1, a comparison between scene text and logo/textual brand elements is presented. The scene text is sourced from the CTW1500 dataset [4], whereas the logo/textual brand elements originate from the METU dataset [5].