As camera-based documents are increasingly used, the rectification of
distorted document images becomes a need to improve the recognition
performance. In this paper, we propose a novel framework for both rectifying
distorted document image and removing background finely, by estimating
pixel-wise displacements using a fully convolutional network (FCN). The
document image is rectified by transformation according to the displacements of
pixels. The FCN is trained by regressing displacements of synthesized distorted
documents, and to control the smoothness of displacements, we propose a Local
Smooth Constraint (LSC) in regularization. Our approach is easy to implement
and consumes moderate computing resource. Experiments proved that our approach
can dewarp document images effectively under various geometric distortions, and
has achieved the state-of-the-art performance in terms of local details and
overall effect.

通过全卷积神经网络 (FCN) 估算像素位移来实现文档图像矫正与背景消除，我们提出了一种新的框架来估算像素位移，训练 FCN 通过回归失真文档的位移，我们提出了局部平滑约束（LSC）作为正则化以控制平滑度。实验表明，我们的方法在各种几何失真下可有效去除文档图像畸变，并在局部细节和总体效果上取得了最先进的性能。