ForCenNet: Foreground-Centric Network for Document Image Rectification
Peng Cai, Qiang Li, Kaicheng Yang, Dong Guo, Jia Li, Nan Zhou, Xiang An, Ninghua Yang, Jiankang Deng
2025-07-29
Summary
This paper talks about ForCenNet, a model designed to fix warped or distorted images of documents by focusing on the main parts of the document, such as the text or drawings in the foreground.
What's the problem?
The problem is that pictures of documents often come out curved or bent because of the way they are taken, which makes it hard to read or process the information on them. Existing methods sometimes fail to straighten these images accurately.
What's the solution?
ForCenNet solves this by concentrating on the foreground parts of the document to clearly separate them from the background. It also uses a technique called curvature consistency loss, which helps the model learn how to straighten curved lines and edges in the document image accurately.
Why it matters?
This matters because better document image rectification makes scanned or photographed documents easier to read and work with. It helps improve things like text recognition, digital archiving, and any application where clear document images are needed.
Abstract
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.