  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.
