Document skew detection based on the fractal and least squares method
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a simple and robust algorithm is presented to detect skew in a totally unconstrained document. It can discover the skew angle not only in the whole page of document but also in different document blocks which have their different skew angles. This method consists of four major phases, namely: (a) skew detection and correction for whole page; (b) segmentation of document into blocks; (c) identification of skewed text blocks, and (d) skew detection and correction for the skewed text blocks. To detect the skew in a document, the saw-tooth algorithm and least squares method are used. To segment a document into blocks, the fractal approach is applied. Promising experimental results are also provided to prove the effectiveness of the proposed method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it