Abstract
Scene text removal (STR) aims to erase textual elements from images. It was originally intended for removing privacy-sensitiveor undesired texts from natural scene images, but is now also appliedto typographic images. STR typically detects text regions and theninpaints them. Although STR has advanced through neural networksand synthetic data, misuse risks have increased. This paper investi-gates Inverse STR (ISTR), which analyzes STR-processed images andfocuses on binary classification (detecting whether an image has un-dergone STR) and localizing removed text regions. We demonstrate inexperiments that these tasks are achievable with high accuracies, en-abling detection of potential misuse and improving STR. We also at-tempt to recover the removed text content by training a text recognizerto understand its difficulty.
Abstract (translated)
场景文本去除（STR）的目标是从图像中移除文字元素。最初，它被设计用来从自然场景图片中删除隐私敏感或不希望存在的文字，但现在也应用于排版图象。STR通常会检测出文字区域然后进行修复。尽管STR通过神经网络和合成数据得到了改进，但滥用的风险也随之增加。本文研究了逆向STR（ISTR），即分析经过STR处理的图像，并专注于二元分类（识别一张图片是否经历过STR处理）以及定位被移除的文字区域。我们在实验中展示了这些任务可以实现高准确率，从而能够检测潜在的滥用行为并改进STR技术。我们还尝试通过训练一个文字识别器来理解恢复删除文本内容的难度。
URL
https://arxiv.org/abs/2506.21002
