Segmentation of Overlapping Pixels in Multi-spectral Document Images by Statistical Techniques
Bibliographic record
Abstract
The examination of hyperspectral data has become a powerful tool in the field of document image processing, offering unprecedented levels of information and insights across various applications. Traditionally, tasks like document forgery detection, ink age estimation, and text extraction from degraded or damaged documents have relied on RGB images. However, as the need for more precise tasks, such as multiclass signature segmentation, arises, there is a demand for a richer source of information to avoid data loss. This paper introduces a new method for segmenting signatures with class overlap in hyperspectral document images. Unlike conventional RGB approaches, our proposed technique is specifically designed for hyperspectral data. To validate the effectiveness of our methodology, we rigorously test the handwritten signatures segmented using our approach against ground truth images. The results confirm that our method is not only effective but also efficient and precise in the challenging task of segmenting signatures with class overlap in hyperspectral document images. This breakthrough significantly enhances the capabilities of hyperspectral data analysis in the domain of document image processing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".