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

Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context