TADoc: Robust Time-Aware Document Image Dewarping

TL;DR

TADoc introduces a novel time-aware, dynamic approach to document image dewarping, modeling it as a progressive process and proposing a new evaluation metric, significantly improving robustness and accuracy on various benchmarks.

Contribution

The paper reformulates document dewarping as a progressive, multi-stage process and introduces TADoc, a lightweight network, along with a new evaluation metric for better assessment.

Findings

TADoc outperforms existing methods on multiple benchmarks.

The new DLS metric better correlates with downstream OCR performance.

Model demonstrates strong robustness across diverse document types and distortions.

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.