# Cascaded Dual-Inpainting Network for Scene Text
## 2 Related Work  
### Image Inpainting  
Deep learning has achieved remarkable progress in image inpainting. Existing methods can be divided into three types: (1) convolutional neural network (CNN)-based approaches, (2) generative adversarial network (GAN)-based frameworks, and (3) diffusion-model-driven techniques. The convolutional neural network (CNN) plays an important role in the image inpainting task. CNN-based image inpainting utilizes localized feature extraction to handle diverse missing regions adaptively. It can capture image information through a multi-scale design, adapting to various types of defect areas and playing an important role in the texture continuity and semantic consistency of the restored images. [PERSON] et al. [1] proposed the U-Net architecture with a symmetric encoder-decoder design, which integrated local details and global information through skip connections that provided an effective approach for contextual capturing and multi-scale feature support in image inpainting. [PERSON] et al. [2] embedded attention mechanisms into the U-Net architecture to enhance the ability of context modeling. The inpainting performance of the missing area was significantly improved. [PERSON] et al. [3] introduced ResNet into image inpainting. It effectively promoted feature integration and texture prediction and further improved the detail quality of restoration. [PERSON] et al. [4] introduced Context Encoders, which utilized a CNN to capture the appearance information and semantic structure features of pixels around the missing area. This was an unsupervised visual feature learning method, but it was prone to blurring issues. [PERSON] et al. [5] proposed Shift-Net, which introduced a dedicated shift connection layer. This architecture could generate the missing part with a clear structure and fine texture, thereby effectively solving the blurring problem caused by previous contextual encoders. For the image inpainting task with irregular masks, [PERSON] et al. [6] proposed a partial convolution method. This approach applied convolution operations exclusively to valid pixels, which avoided the negative impact of filled values in masked holes on feature extraction. However, partial convolution suffered from rigid mask-updating rules, which made it hard to adaptively learn diverse mask shapes. [PERSON] et al. [7] proposed gated convolution, which employed a dynamic feature selection mechanism to adaptively adjust feature weights for corrupted regions. This approach significantly enhanced the model's adaptability to free-form masks and its capability to capture semantic information. The proposed method improved the detail quality of image inpainting and the capacity for complex scenes. With the development of deep learning techniques and the growing demand for diverse scenarios in image inpainting tasks, the limitations of single-model approaches have become evident. CNN-based methods struggle to effectively capture global contextual information. It is a current trend shifting toward multi-module integration frameworks that can integrate the merits of different foundational models. GAN-based image inpainting leverages generative adversarial networks (GANs) to generate realistic content within missing image regions. The GAN frameworks, initially proposed by [PERSON] et al. [8], operate through two neural networks in adversarial competition: a generator that creates synthetic content and a discriminator that distinguishes real from generated data. This adversarial training mechanism enables the system to progressively improve the authenticity of generated outputs. [PERSON] et al. [9] applied dilated convolution, globally and locally context discriminators, to image inpainting. This approach restored missing image portions of arbitrary resolutions and shapes while maintaining global semantic consistency. This approach captured broader contextual information while preserving computational efficiency without increasing parameters or computational costs.