Modeling segmentation cuts using support vector machines
Bibliographic record
Abstract
In this paper we propose a method to evaluate segmentation cuts for handwritten touching digits. The idea of this method is to work as a filter in segmentation-based recognition systems. These types of systems usually rely on over-segmentation methods, where several segmentation hypotheses are created for each touching group of digits and then assessed by a general-purpose classifier. Through the use of the proposed method, unnecessary segmentation cuts can be identified without any attempt of classification by a general-purpose classifier, reducing the number of paths in a segmentation graph, what can consequently lead to a reduction in computational cost. Concavity analysis is performed in each digit before and after segmentation. The difference of those concavities is used to model the segmentation cuts. SVM is used to classify those segmentation cuts. The preliminary results obtained are very promising as for the segmentation algorithm tested, 67.9 % of the unnecessary segmentation cuts were eliminated. Moreover, it was possible to achieve a significant increase in the recognition rate for the generalpurpose 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".