ForCenNet: Foreground-Centric Network for Document Image Rectification
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.
68883519af872d625c10c5f9
Peng Cai
68883519af872d625c10c5fa
Qiang Li
68883519af872d625c10c5fb
Kaicheng Yang
claimed_verified
2025-07-29T07:08:21.957Z
63e202f352b7578dba448ab5
https://cdn-avatars.huggingface.co/v1/production/uploads/63e202f352b7578dba448ab5/8itVBLcv14m7OVsoF8h1o.jpeg
Yang
user
Kaichengalex
68883519af872d625c10c5fc
Dong Guo
68883519af872d625c10c5fd
Jia Li
68883519af872d625c10c5fe
Nan Zhou
68883519af872d625c10c5ff
Xiang An
68883519af872d625c10c600
Ninghua Yang
68883519af872d625c10c601
Jiankang Deng
ForCenNet: Foreground-Centric Network for Document Image Rectification
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.
ForCenNet, a foreground-centric network, improves document image rectification by enhancing foreground element distinction and using curvature consistency loss, achieving state-of-the-art results on real-world benchmarks.
foreground-centric label generation foreground-centric mask mechanism curvature consistency loss document image rectification text recognition DocUNet DIR300 WarpDoc DocReal