Time and Accuracy Analysis of Skew Detection Methods for Document Images
Why this work is in the frame
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Bibliographic record
Abstract
Detecting skew angle in a document image has been an area of research interest for a long time. This paper presents an experimental analysis of various existing skew detection techniques involving methods such as Radon transform, Hough transform, Principal Component Analysis (PCA), PCA with Wavelet transform and Moments with Wavelet transform. Detailed analysis of existing skew detection method against the parameters time complexity, space complexity, robustness, accuracy, flexibility, etc. has been carried out for seven different categories of digital documents. The categories of these documents spans from those containing handwritten text in different languages, to the ones with both text and pictures. Radon transform is observed to be the fastest method when the image size is small and works with virtually all types of documents. It is an accurate method as well as works faster, even with the document containing pictures. PCA method is also faster than Hough transform for machine printed documents but used less for real time skew distortion due to its limitations. If the document image size is large, then Moments with Wavelet transform has better time complexity than other methods, but do not work well with documents containing images. Hough transform is the most accurate method, though it is computationally expensive.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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