Multi-script writer identification using dissimilarity
Why this work is in the frame
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Bibliographic record
Abstract
Multi-script writer identification consists in identifying a person of a given text written in one script from the samples of the same person written in another script. The rationale behind this is that the writing style of an individual remains constant across different scripts. While this hypothesis may hold, recent results on a multi-script writer identification competition show that classical writer-dependent classifiers fail in this task. In this work we investigate the efficacy of a writer-independent classifier based on dissimilarity for multi-script writer identification. The classifiers were trained using two different texture descriptors (LBP and LPQ). Our experiments on 475 writers of the QUWI dataset, which is composed of Arabic and English samples, show that the proposed strategy surpasses the results published in the literature by a large margin, achieving error rates similar to single-script writer identification systems.
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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 | 0.000 | 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 | 0.001 | 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