SHOCK FILTER-BASED DIFFUSION FIELDS — APPLICATION TO GRAYSCALE CHARACTER IMAGE PROCESSING
Bibliographic record
Abstract
In this article, the new concept of diffusion fields based on partial differential equations is applied to character image processing. Specific diffusion fields are developed according to character image structures and features, depending, on the scope of application. Doing so allows the application of a straightforward one-dimensional numerical scheme to image enhancement, erosion, dilation and thinning. The strength of this approach is the flexibility brought by the diffusion field, which can be defined taking into account specific difficulties of grayscale character images with a minimum of prior information. Thus, the application of the algorithm is shown to be robust to singularity points, the creation of spurious branches, variations in stroke thickness and intensity, multimodality, noise and image background patterns. The resulting enhanced images are noise free with sharp edges and the local typical intensity levels preserved. Thinned characters are connected skeletons located on the ridge of the initial character. Again, the typical intensity of the character and background are preserved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".