Fourier Document Restoration for Robust Document Dewarping and Recognition
Chuhui Xue, Zichen Tian, Fangneng Zhan, Shijian Lu, Song Bai

TL;DR
FDRNet is a Fourier-based document restoration network that effectively dewarps and recognizes distorted documents using high-frequency Fourier components and a flexible transformation, requiring minimal training data.
Contribution
The paper introduces FDRNet, a novel Fourier-based approach that improves document dewarping and recognition with less training data and no need for deformation annotations.
Findings
FDRNet outperforms state-of-the-art methods on dewarping and recognition tasks.
It effectively handles complex geometric distortions.
Requires only a small amount of labeled training data.
Abstract
State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth. This paper presents FDRNet, a Fourier Document Restoration Network that can restore documents with different distortions and improve document recognition in a reliable and simpler manner. FDRNet focuses on high-frequency components in the Fourier space that capture most structural information but are largely free of degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline transformation which can handle various deformations effectively without requiring deformation annotations in training. These features allow FDRNet to learn from a small amount of simply labeled training images, and the learned model can dewarp documents with complex geometric…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Image Processing and 3D Reconstruction · Geophysical Methods and Applications
