A precise, controllable, interpretable and easily trainable text removal approach is necessary for both user-specific and large-scale text removal applications. To achieve this, we propose a one-stage mask-based text inpainting network, MTRNet++. It has a novel architecture that includes mask-refine, coarse-inpainting and fine-inpainting branches, and attention blocks. With this architecture, MTRNet++ can remove text either with or without an external mask. It achieves state-of-the-art results on both the Oxford and SCUT datasets without using external ground-truth masks. The results of ablation studies demonstrate that the proposed multi-branch architecture with attention blocks is effective and essential. It also demonstrates controllability and interpretability.

对于特定于用户的和大规模的文本删除应用程序，需要一种精确，可控，可解释且易于培训的文本删除方法。为此，我们提出了一个基于蒙版的单阶段文本修复网络MTRNet ++。它具有一种新颖的体系结构，包括蒙版细化，粗糙绘画和精细绘画分支以及关注块。借助这种体系结构，MTRNet ++可以删除带有或不带有外部掩码的文本。它在牛津和SCUT数据集上都获得了最新的结果，而无需使用外部的真实蒙版。消融研究的结果表明，所提出的带有注意力障碍的多分支架构是有效且必不可少的。它还展示了可控性和可解释性。