DocUNet: Document Image Unwarping via a Stacked U-Net

Ke Ma, Zhixin Shu, Xue Bai, Jue Wang, Dimitris Samaras; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4700-4709

Abstract


Capturing document images is a common way for digitizing and recording physical documents due to the ubiquitousness of mobile cameras. To make text recognition easier, it is often desirable to digitally flatten a document image when the physical document sheet is folded or curved. In this paper, we develop the first learning-based method to achieve this goal. We propose a stacked U-Net with intermediate supervision to directly predict the forward mapping from a distorted image to its rectified version. Because large-scale real-world data with ground truth deformation is difficult to obtain, we create a synthetic dataset with approximately 100 thousand images by warping non-distorted document images. The network is trained on this dataset with various data augmentations to improve its generalization ability. We further create a comprehensive benchmark that covers various real-world conditions. We evaluate the proposed model quantitatively and qualitatively on the proposed benchmark, and compare it with previous non-learning-based methods.

Related Material


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[bibtex]
@InProceedings{Ma_2018_CVPR,
author = {Ma, Ke and Shu, Zhixin and Bai, Xue and Wang, Jue and Samaras, Dimitris},
title = {DocUNet: Document Image Unwarping via a Stacked U-Net},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}