# Cascaded Dual-Inpainting Network for Scene Text
###### Abstract  
Scene text inpainting is a significant research challenge in visual text processing, with critical applications spanning incomplete traffic sign comprehension, degraded container-code recognition, occluded vehicle license plate processing, and other incomplete scene text processing systems. In this paper, a cascaded dual-inpainting network for scene text (CDINST) is proposed. The architecture integrates two scene text inpainting models to reconstruct the text foreground: the Structure Generation Module (SGM) and Structure Reconstruction Module (SRM). The SGM primarily performs preliminary foreground text reconstruction and extracts text structures. Building upon the SGM's guidance, the SRM subsequently enhances the foreground structure reconstruction through structure-guided refinement. The experimental results demonstrate compelling performance on the benchmark dataset, showcasing both the effectiveness of the proposed dual-inpainting network and its accuracy in incomplete scene text recognition. The proposed network achieves an average recognition accuracy improvement of 11.94% compared to baseline methods for incomplete scene text recognition tasks. s _accepted_28 May 2025  
3 July 20258 July 20  
variations, scene text remains recognizable through its inherent structures and stroke patterns. The same text content may have various styles. This inherent variability increases the difficulties in incomplete scene text inpainting. Second, corruption in incomplete scene text images manifests multifaceted uncertainties, such as unpredictable corruption localization, corruption scale instability, irregular geometric corruption, stochastic corruption ratios, and variable corruption patterns. These stochastic and uncertain corruptions seriously impair character recognition accuracy, since fragmented strokes are unpredictable. Third, texts exhibit dual characteristics of texture commonality and structural specificity, which make texts have similar global features and different local features. These structural and textural properties prove crucial for scene text detection and recognition systems, as they establish fundamental dependencies on these dual characteristics. Severe destruction of these structural and textural attributes undermines text recognition performance. Therefore, it is essential to develop specialized scene text inpainting methods. In this paper, a cascaded dual-inpainting network is proposed to address the incomplete scene text inpainting. The proposed framework comprises two specialized text inpainting modules to enhance downstream recognition performance: (1) the Structure Generation Module (SGM) for text structure prediction, and (2) the Structure Reconstruction Module (SRM) for text structure reconstruction. The SGM generates text structure regions and predicts missing structural content through an encoder-decoder architecture. This module achieves two key objectives: constructing complete text structure layouts and recovering missing structural patterns. Guided by the SGM-generated text structure, the SRM further refines the reconstruction through structure-guided optimization of geometric consistency and perceptual quality. Within this framework, the SGM's structural output directly guides the SRM to enhance reconstruction fidelity. Although the two models are architecturally cascaded in design, they maintain independent operational frameworks. Specifically, the SRM is explicitly conditioned on the SGM's output during the inference phase. The contributions are summarized as follows. The first contribution is the proposal of a novel cascaded dual-inpainting framework for scene text structure extraction and inpainting (CDINST), consisting of two specialized components: the SGM and the SRM. The second contribution is the proposal of the SRM, which enhances foreground structure reconstruction through structure-guided refinement and attention mechanisms. The SGM-generated text structures provide guidance to the SRM for precise structural reconstruction.