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The Parser Learned the Camera Bent the Page
NaviDC-OCR models geometric deformation, high-resolution sampling and document structure in one parsing system.
Summary
NaviDC-OCR models geometric deformation, high-resolution sampling and document structure in one parsing system.
The framework targets both digital files and photographs, where perspective and warping can make layout errors cascade. It combines deformation-aware learning, adaptive sampling and separate modeling for content, formulas and tables. The paper reports scores of 96.87, 88.53 and 78.41 on three document benchmarks and first place in the ICDAR 2026 Sci-ImageMiner Challenge; these are benchmark results, not proof of error-free parsing in every capture condition.
Why it matters
NaviDC-OCR models geometric deformation, high-resolution sampling and document structure in one parsing system.
Limits and context
- The paper reports scores of 96.87, 88.53 and 78.41 on three document benchmarks and first place in the ICDAR 2026 Sci-ImageMiner Challenge; these are benchmark results, not proof of error-free parsing in every capture condition.
Key claims
NaviDC-OCR models geometric deformation, high-resolution sampling and document structure in one parsing system.
Qualification: The paper reports scores of 96.87, 88.53 and 78.41 on three document benchmarks and first place in the ICDAR 2026 Sci-ImageMiner Challenge; these are benchmark results, not proof of error-free parsing in every capture condition.
Evidence: source-2026-08-15-014
Sources
- arXiv preprint 2608.12898arXiv · primary research
Corrections
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