TADoc: Robust Time-Aware Document Image Dewarping

• URL: http://arxiv.org/abs/2508.06988v1
• Date: Sat, 09 Aug 2025 13:55:55 GMT
• Title: TADoc: Robust Time-Aware Document Image Dewarping
• Authors: Fangmin Zhao, Weichao Zeng, Zhenhang Li, Dongbao Yang, Yu Zhou,
• Abstract summary: Document image dewarping is an increasingly important task with the rise of digital economy and online working.<n>We reformulate this task, modeling it for the first time as a dynamic process that encompasses a series of intermediate states.<n>We design a lightweight framework called TADoc to address the geometric distortion of document images.
• Score: 4.080803969466669
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Flattening curved, wrinkled, and rotated document images captured by portable photographing devices, termed document image dewarping, has become an increasingly important task with the rise of digital economy and online working. Although many methods have been proposed recently, they often struggle to achieve satisfactory results when confronted with intricate document structures and higher degrees of deformation in real-world scenarios. Our main insight is that, unlike other document restoration tasks (e.g., deblurring), dewarping in real physical scenes is a progressive motion rather than a one-step transformation. Based on this, we have undertaken two key initiatives. Firstly, we reformulate this task, modeling it for the first time as a dynamic process that encompasses a series of intermediate states. Secondly, we design a lightweight framework called TADoc (Time-Aware Document Dewarping Network) to address the geometric distortion of document images. In addition, due to the inadequacy of OCR metrics for document images containing sparse text, the comprehensiveness of evaluation is insufficient. To address this shortcoming, we propose a new metric -- DLS (Document Layout Similarity) -- to evaluate the effectiveness of document dewarping in downstream tasks. Extensive experiments and in-depth evaluations have been conducted and the results indicate that our model possesses strong robustness, achieving superiority on several benchmarks with different document types and degrees of distortion.

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