A comparative study of the k nearest neighbour, threshold and neural network classifiers for handwritten signature verification using an enhanced directional PDF
Bibliographic record
Abstract
A neural network approach is proposed to build the first stage of an automatic handwritten signature verification system that will eliminate random and simple forgeries rapidly. The directional probability density function was used as a global shape factor, and its discriminatory power was enhanced by reducing its cardinality. The choice of the best pretreatment was made by means of a k nearest neighbour classifier. This study has shown that the cardinality of the PDF can be reduced by a factor of ten while doubling its discriminatory power. The backpropagation model was retained to build the neural network classifier. An experimental protocol was used to find the best configuration of the BPN classifier whose performance was compared on the same database and with the same decision rule (without rejection criteria), to those of the kNN and threshold classifiers. This comparison shows that the BPN classifier is clearly better than the T classifier, and compares favourably with the kNN classifier.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".