  Text removal has attracted increasingly attention due to its various
applications on privacy protection, document restoration, and text editing. It
has shown significant progress with deep neural network. However, most of the
existing methods often generate inconsistent results for complex background. To
address this issue, we propose a Contextual-guided Text Removal Network, termed
as CTRNet. CTRNet explores both low-level structure and high-level
discriminative context feature as prior knowledge to guide the process of
background restoration. We further propose a Local-global Content Modeling
(LGCM) block with CNNs and Transformer-Encoder to capture local features and
establish the long-term relationship among pixels globally. Finally, we
incorporate LGCM with context guidance for feature modeling and decoding.
Experiments on benchmark datasets, SCUT-EnsText and SCUT-Syn show that CTRNet
significantly outperforms the existing state-of-the-art methods. Furthermore, a
qualitative experiment on examination papers also demonstrates the
generalization ability of our method. The codes and supplement materials are
available at https://github.com/lcy0604/CTRNet.
