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State-of-the-art document dewarping techniques learn to predict 3-dimensionalinformation of documents which are prone to errors while dealing with documentswith irregular distortions or large variations in depth. This paper presentsFDRNet, a Fourier Document Restoration Network that can restore documents withdifferent distortions and improve document recognition in a reliable andsimpler manner. FDRNet focuses on high-frequency components in the Fourierspace that capture most structural information but are largely free ofdegradation in appearance. It dewarps documents by a flexible Thin-Plate Splinetransformation which can handle various deformations effectively withoutrequiring deformation annotations in training. These features allow FDRNet tolearn from a small amount of simply labeled training images, and the learnedmodel can dewarp documents with complex geometric distortion and recognize therestored texts accurately. To facilitate document restoration research, wecreate a benchmark dataset consisting of over one thousand camera documentswith different types of geometric and photometric distortion. Extensiveexperiments show that FDRNet outperforms the state-of-the-art by large marginson both dewarping and text recognition tasks. In addition, FDRNet requires asmall amount of simply labeled training data and is easy to deploy.

Fourier Document Restoration for Robust Document Dewarping and Recognition