Text Image Inpainting via Global Structure-Guided Diffusion Models

Abstract: Real-world text can be damaged by corrosion issues caused by environmental or human factors, which hinder the preservation of the complete styles of texts, e.g., texture and structure. These corrosion issues, such as graffiti signs and incomplete signatures, bring difficulties in understanding the texts, thereby posing significant challenges to downstream applications, e.g., scene text recognition and signature identification. Notably, current inpainting techniques often fail to adequately address this problem and have difficulties restoring accurate text images along with reasonable and consistent styles. Formulating this as an open problem of text image inpainting, this paper aims to build a benchmark to facilitate its study. In doing so, we establish two specific text inpainting datasets which contain scene text images and handwritten text images, respectively. Each of them includes images revamped by real-life and synthetic datasets, featuring pairs of original images, corrupted images, and other assistant information. On top of the datasets, we further develop a novel neural framework, Global Structure-guided Diffusion Model (GSDM), as a potential solution. Leveraging the global structure of the text as a prior, the proposed GSDM develops an efficient diffusion model to recover clean texts. The efficacy of our approach is demonstrated by thorough empirical study, including a substantial boost in both recognition accuracy and image quality. These findings not only highlight the effectiveness of our method but also underscore its potential to enhance the broader field of text image understanding and processing. Code and datasets are available at: https://github.com/blackprotoss/GSDM.

• The paper introduces the Global Structure-guided Diffusion Model (GSDM) that leverages structural cues for effective text image restoration.
• The paper develops two novel datasets, TII-ST and TII-HT, to evaluate inpainting techniques on both scene and handwritten texts under diverse degradations.
• The paper demonstrates that GSDM outperforms existing methods with enhanced PSNR, SSIM, and improved accuracy on downstream text recognition tasks.

Overview of "Text Image Inpainting via Global Structure-Guided Diffusion Models"

The paper "Text Image Inpainting via Global Structure-Guided Diffusion Models," authored by Shipeng Zhu et al., addresses the complex challenge of text image inpainting. The primary focus is on restoring corrupted text images that have been affected by environmental and human-induced corrosion, impacting both scene and handwritten texts. The authors introduce novel datasets and a neural framework to tackle the nuanced demands of this task, emphasizing the importance of maintaining consistent text styles and structures during the inpainting process.

Contributions and Methodology

One of the significant contributions of this work is the introduction of two dedicated datasets—TII-ST and TII-HT. These datasets encompass both synthesized and real-world text images, characterized by various forms of corrosion such as convex hulls, irregular regions, and quick draws. These curated datasets enable a comprehensive evaluation of inpainting methods on text images, presenting nuances representative of real-world degradation.

The paper proposes the Global Structure-guided Diffusion Model (GSDM), a sophisticated method for text image inpainting. This model leverages the inherent structures within text images as a prior, enabling effective restoration of the text's visual integrity. The GSDM comprises two core components: the Structure Prediction Module (SPM) and the Reconstruction Module (RM). The SPM uses U-Net with dilated convolutions to predict complete segmentation maps of text images, serving as structural guidance for the RM. The reconstruction process is further optimized through a diffusion-based method, ensuring high-quality and coherent image reconstruction. The diffusion model is tailored to predict image content rather than noise, enhancing the robustness of the generated outputs.

Results and Implications

Empirical results demonstrate that the proposed GSDM significantly outperforms existing inpainting methods, such as CoPaint, TransCNN-HAE, and DDIM, both in terms of image quality and recognition accuracy on downstream tasks. The paper provides comprehensive evaluations using metrics like PSNR and SSIM, alongside recognition performance from models like ASTER and MORAN for scene text, and DAN and TrOCR models for handwritten text. Noteworthy is GSDM's ability to handle varying corrosion ratios and forms effectively, maintaining its superior performance across these different conditions.

The implications of this research are substantial for fields that rely on accurate text image processing, such as digital preservation, automated documentation processing, and real-time text analysis in augmented reality systems. By emphasizing consistency in style and content reconstruction, the GSDM stands to significantly improve the fidelity of text recognition systems in challenging environments.

Future Directions

The work opens several avenues for future exploration. Enhancements in model architecture could further address the computational efficiency challenges associated with diffusion models, potentially through hybrid approaches that integrate other generative models. Additionally, expanding the datasets to cover more languages and text styles could broaden the applicability of the proposed methods. Furthermore, exploring the synergy between text inpainting and other text-based tasks could offer integrated solutions for comprehensive text document restoration.

In conclusion, the paper presents a robust framework for text image inpainting, supported by detailed empirical analyses and valuable dataset contributions. The approach and findings provide a solid foundation for further advancements in text image restoration, promising enhanced performance in both academic and practical domains.