DocRevive: A Unified Pipeline for Document Text Restoration

• URL: http://arxiv.org/abs/2604.10077v1
• Date: Sat, 11 Apr 2026 07:50:20 GMT
• Title: DocRevive: A Unified Pipeline for Document Text Restoration
• Abstract summary: This paper presents a novel unified pipeline combining state-of-the-art Optical Character Recognition (OCR) and advanced image analysis.<n>We create a synthetic dataset of 30,078 degraded document images that simulates diverse document degradation scenarios.<n>A diffusion-based module seamlessly reintegrates text, matching font, size, and alignment.
• Score: 8.164723249655319
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In Document Understanding, the challenge of reconstructing damaged, occluded, or incomplete text remains a critical yet unexplored problem. Subsequent document understanding tasks can benefit from a document reconstruction process. In response, this paper presents a novel unified pipeline combining state-of-the-art Optical Character Recognition (OCR), advanced image analysis, masked language modeling, and diffusion-based models to restore and reconstruct text while preserving visual integrity. We create a synthetic dataset of 30{,}078 degraded document images that simulates diverse document degradation scenarios, setting a benchmark for restoration tasks. Our pipeline detects and recognizes text, identifies degradation with an occlusion detector, and uses an inpainting model for semantically coherent reconstruction. A diffusion-based module seamlessly reintegrates text, matching font, size, and alignment. To evaluate restoration quality, we propose a Unified Context Similarity Metric (UCSM), incorporating edit, semantic, and length similarities with a contextual predictability measure that penalizes deviations when the correct text is contextually obvious. Our work advances document restoration, benefiting archival research and digital preservation while setting a new standard for text reconstruction. The OPRB dataset and code are available at \href{https://huggingface.co/datasets/kpurkayastha/OPRB}{Hugging Face} and \href{https://github.com/kunalpurkayastha/DocRevive}{Github} respectively.

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