Recognition of rotated characters by Eigen-space
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a method of recognizinginclined, rotated characters. First we construct an eigensub-space for each category using the covariance matrixwhich is calculated from a sufficient number of rotatedcharacters. Next, we can obtain a locus by projectingtheir rotated characters onto the eigen sub-space andinterpolating between their projected points. An unknowncharacter is also projected onto the eigen sub-space ofeach category. Then, the verification is carried out bycalculating the distance between the projected point ofthe unknown character and the locus. In our experiment,we obtained quite good results for the CENTURY font of26 capital letters of the English alphabet (A, B, .... ,Z).This method has the added advantage of obtaining therecognition result (category) and angle of inclination atthe same time
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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 | 0.000 |
| 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 it