CTRNet++: Dual-Path Learning with Local-Global Context Modeling for Scene Text Removal

Recent advances in scene text removal have attracted growing research interest due to its applications on privacy protection, document restoration, and text editing. While deep learning and generative adversarial network have shown significant progress, existing methods often struggle to generate consistent and plausible textures when erasing texts on complex backgrounds. To address this challenge, we propose a Contextual-guided Text Removal Network (CTRNet). CTRNet utilizes Low-level/High-level Contextual Guidance blocks (LCG, HCG) to explore both low-level structure and high-level discriminative context features from existing data to guide the text erasure and background restoration process. We further extend CTRNet to CTRNet++ by incorporate an auto-encoder architecture as a novel and effective HCG block, which serves as an additional image-inpainting branch, providing more accurate texture and context clues with the assistance of a large volume of natural images. Then we introduce a Context Embedding and Content Feature Modeling (CECFM) block that combines depth-wise CNN and Transformer layers to capture local features and establish long-term relationships among pixels globally. In addition, an efficient Progressive Feature Fusion Module (PFFM) is proposed to fully utilize multi-scale features from different branches. Experiments on benchmark datasets, SCUT-EnsText and SCUT-Syn, demonstrate that CTRNet++ significantly outperforms existing state-of-the-art methods and exhibits a stronger ability for complex background reconstruction. The code is available at https://github.com/lcy0604/CTRNet-plus .