Cascaded Robust Rectification for Arbitrary Document Images

• URL: http://arxiv.org/abs/2511.23150v1
• Date: Fri, 28 Nov 2025 12:56:16 GMT
• Title: Cascaded Robust Rectification for Arbitrary Document Images
• Authors: Chaoyun Wang, Quanxin Huang, I-Chao Shen, Takeo Igarashi, Nanning Zheng, Caigui Jiang,
• Abstract summary: Document rectification in real-world scenarios poses significant challenges due to extreme variations in camera perspectives and physical distortions.<n>We introduce a novel multi-stage framework that progressively reverses distinct distortion types in a coarse-to-fine manner.<n>Our framework first performs a global affine transformation to correct perspective distortions arising from the camera's viewpoint, then rectifies geometric deformations resulting from physical paper curling and folding, and finally employs a content-aware iterative process to eliminate fine-grained content distortions.
• Score: 45.30113042855903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document rectification in real-world scenarios poses significant challenges due to extreme variations in camera perspectives and physical distortions. Driven by the insight that complex transformations can be decomposed and resolved progressively, we introduce a novel multi-stage framework that progressively reverses distinct distortion types in a coarse-to-fine manner. Specifically, our framework first performs a global affine transformation to correct perspective distortions arising from the camera's viewpoint, then rectifies geometric deformations resulting from physical paper curling and folding, and finally employs a content-aware iterative process to eliminate fine-grained content distortions. To address limitations in existing evaluation protocols, we also propose two enhanced metrics: layout-aligned OCR metrics (AED/ACER) for a stable assessment that decouples geometric rectification quality from the layout analysis errors of OCR engines, and masked AD/AAD (AD-M/AAD-M) tailored for accurately evaluating geometric distortions in documents with incomplete boundaries. Extensive experiments show that our method establishes new state-of-the-art performance on multiple challenging benchmarks, yielding a substantial reduction of 14.1\%--34.7\% in the AAD metric and demonstrating superior efficacy in real-world applications. The code will be publicly available at https://github.com/chaoyunwang/ArbDR.

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