ForCenNet: Foreground-Centric Network for Document Image Rectification
Authors: name: Peng Cai, name: Qiang Li, name: Kaicheng Yang, name: Dong Guo, name: Jia Li, name: Nan Zhou, name: Xiang An, name: Ninghua Yang, name: Jiankang Deng

Document image rectification aims to eliminate geometric deformation in
photographed documents to facilitate text recognition. However, existing
methods often neglect the significance of foreground elements, which provide
essential geometric references and layout information for document image
correction. In this paper, we introduce Foreground-Centric Network (ForCenNet)
to eliminate geometric distortions in document images. Specifically, we
initially propose a foreground-centric label generation method, which extracts
detailed foreground elements from an undistorted image. Then we introduce a
foreground-centric mask mechanism to enhance the distinction between readable
and background regions. Furthermore, we design a curvature consistency loss to
leverage the detailed foreground labels to help the model understand the
distorted geometric distribution. Extensive experiments demonstrate that
ForCenNet achieves new state-of-the-art on four real-world benchmarks, such as
DocUNet, DIR300, WarpDoc, and DocReal. Quantitative analysis shows that the
proposed method effectively undistorts layout elements, such as text lines and
table borders. The resources for further comparison are provided at
https://github.com/caipeng328/ForCenNet.