Improving Handwritten Signatures Fluency via the Lognormality Principle
Bibliographic record
Abstract
This chapter proposes two efficient methods to modify the fluency of dynamic signatures. The main idea is to modify the number of velocity minima or virtual target points and reconstruct the signature with the new virtual target points. If the number of virtual target points is reduced, the fluency is improved, and vice versa. The modification of the virtual target points is accomplished initially by linking the samples of an on-line signature by 8-connected Bresenham’s lines to obtain the interpolated trajectory. Then, the most perceptually important points are estimated from the corners in the trajectory. To this end, two methods are proposed. The first method, which we term resampling-wise, develops a lognormal synthetic velocity profile used for resampling the static trajectory. The second method, recovering-wise, consists in estimating the virtual target points from the perceptually important points in the trajectory, linking them by circular trajectories, and reconstructing the dynamic trajectory. Additionally, both methods can be used to generate synthetic on-line signatures from static trajectories. Both methods’ efficiency has been tested in automatic signature verification by increasing skilled forgeries’ fluidity with the proposed methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".