Abstract
Text removal is a crucial task in computer vision with applications such as privacy preservation, image editing, and media reuse. While existing research has primarily focused on scene text removal in natural images, limitations in current datasets hinder out-of-domain generalization or accurate evaluation. In particular, widely used benchmarks such as SCUT-EnsText suffer from ground truth artifacts due to manual editing, overly simplistic text backgrounds, and evaluation metrics that do not capture the quality of generated results. To address these issues, we introduce an approach to synthesizing a text removal benchmark applicable to domains other than scene texts. Our dataset features text rendered on complex backgrounds using object-aware placement and vision-language model-generated content, ensuring clean ground truth and challenging text removal scenarios. The dataset is available at this https URL .
Abstract (translated)
文本移除是计算机视觉中的一个关键任务，具有隐私保护、图像编辑和媒体重用等多种应用。尽管现有研究主要集中在自然图像中场景文字的去除上，但当前数据集的局限性阻碍了跨域泛化或准确评估。尤其是广泛使用的基准如SCUT-EnsText，由于人工编辑导致的真实标签瑕疵、过于简单的文本背景以及无法捕捉生成结果质量的评价指标等问题。 为解决这些问题，我们提出了一种合成适用于除场景文字外其他领域的文本移除基准的方法。我们的数据集特点是使用对象感知放置和视觉语言模型生成的内容，在复杂背景下渲染文本，确保真实标签干净且提供挑战性的文本移除场景。该数据集可在此网址获取：[请在此处插入实际的URL链接]。
URL
https://arxiv.org/abs/2510.02787
