Off-line recognition of handwritten middle age Persian characters using moment
Bibliographic record
Abstract
In this paper, the performance of several moment invariant features combined with various classification methods for the recognition of middle age Persian manuscripts is presented. Specifically, Legendre moments (order 2 to 12), Zernike and pseudo-Zernike moments (order 2 to 15), and the set of invariant moments (ϕ 1 , ϕ 2 , ..., ϕ 7 ) are used as features. These features are computed from four versions of character images; (1) grayscale character images (Set A), (2) semithresholded character images (Set B), (3) binarized character images (Set C), (4) character skeleton (Set D). For classification, we have used the minimum Mean Distance (MMD), k-nearest neighbor (KNN), and Parzen methods. The experiment yielded a 2.86% error rate (97.14% classification rate) with pseudo-Zernike moments on the semithresholded character images (set B).
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".