Axis-Aligned Document Dewarping

• URL: http://arxiv.org/abs/2507.15000v1
• Date: Sun, 20 Jul 2025 15:12:57 GMT
• Title: Axis-Aligned Document Dewarping
• Authors: Chaoyun Wang, I-Chao Shen, Takeo Igarashi, Nanning Zheng, Caigui Jiang,
• Abstract summary: We introduce a new metric, Axis-Aligned Distortion (AAD), that incorporates geometric meaning and aligns with human visual perception.<n>Our method achieves SOTA results on multiple existing benchmarks and achieves 18.2%34.5% improvements on the AAD metric.
• Score: 39.058312371271825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document dewarping is crucial for many applications. However, existing learning-based methods primarily rely on supervised regression with annotated data without leveraging the inherent geometric properties in physical documents to the dewarping process. Our key insight is that a well-dewarped document is characterized by transforming distorted feature lines into axis-aligned ones. This property aligns with the inherent axis-aligned nature of the discrete grid geometry in planar documents. In the training phase, we propose an axis-aligned geometric constraint to enhance document dewarping. In the inference phase, we propose an axis alignment preprocessing strategy to reduce the dewarping difficulty. In the evaluation phase, we introduce a new metric, Axis-Aligned Distortion (AAD), that not only incorporates geometric meaning and aligns with human visual perception but also demonstrates greater robustness. As a result, our method achieves SOTA results on multiple existing benchmarks and achieves 18.2%~34.5% improvements on the AAD metric.

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