NATURAL SKELETONIZATION: NEW APPROACH FOR THE SKELETONIZATION OF HANDWRITTEN CHARACTERS
Bibliographic record
Abstract
In this paper we propose a new algorithm for the skeletonization of handwritten characters. Unlike traditional skeletonization algorithms that relay only on the configuration of a binary image pixel in deciding whether it is deletable or not, Natural Skeletonization (NS) integrates the gray-level information in this process. The underlying principle here, which stems from the elongated properties of the handwritten characters, is that medial pixels of a handwritten stroke are "naturally" darker than its side pixels. NS consists of three steps: (1) the decomposition step; (2) the thinning step; (3) the reconstruction step. The integration of gray-level information is facilitated by the iterative binarization at equally spaced thresholds, which highlights positional differences between the medial and side pixels of a stroke. The advantage of our approach over existing methods is demonstrated by its ability to prevent the "flooding water" and to prevent the boundary noise from developing extraneous branches. One important aspect of the approach is that it relaxes the skeletonization's dependence on the quality and shape of initial binary pattern. The experimental results indicate that the proposed algorithm substantially improves the skeletonization quality compared to experiments with traditional skeletonization methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".