Rectification of Camera-Captured Document Images with Mixed Contents and Varied Layouts
Bibliographic record
Abstract
This paper focuses on the rectification of camera-captured document images with varied layouts of mixed contents. Document images acquired via cameras, including smartphones, are typically plagued by perspective, geometric, and/or rotational distortion that hinders document analysis processes. In this paper, we propose an approach to camera-captured image rectification of text and non-text regions that handles perspective, geometric and rotational distortions present in planar and curled documents, extending a state-of-the-art content-based rectification method. We define surface projections via a three-tiered local transformation model, in which primary curved surface projections are formed from individual text regions, and secondary and tertiary surface projections are formed from non-text regions, resulting in a 'patchwork' combination of surfaces spanning the document image. This transformation model allows us to process document images with varied layouts of mixed contents, including large images and graphics, that also contain some justified text. Experiments and comparisons with a state-of-the-art content-based rectification approach on the public IUPR dataset demonstrate the value of the proposed approach on two levels: 1) a significantly improved rectification performance using standard optical character recognition metrics, along with increased document readability, and 2) an improved range of applicability, i.e. ability to correct document images showing various layouts and content types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".