Improving isolated and in-context classication of handwritten characters
Bibliographic record
Abstract
Earlier work has shown how to recognize handwritten characters by representing coordinate functions or integral invariants as truncated orthogonal series. The series basis functions are orthogonal polynomials defined by a Legendre-Sobolev inner product. It has been shown that the free parameter in the inner product, the 'jet scale', has an impact on recognition both using coordinate functions and integral invariants. This paper develops methods of improving series-based recognition. For isolated classification, the first consideration is to identify optimal values for the jet scale in different settings. For coordinate functions, we find the optimum to be in a small interval with the precise value not strongly correlated to the geometric complexity of the character. For integral invariants, used in orientation-independent recognition, we find the optimal value of the jet scale for each invariant. Furthermore, we examine the optimal degree for the truncated series. For in-context classification, we develop a rotation-invariant algorithm that takes advantage of sequences of samples that are subject to similar distortion. The algorithm yields significant improvement over orientation-independent isolated recognition and can be extended to shear and, more generally, affine transformations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".