Automatic Segmentation of Unconstrained Handwritten Numeral Strings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A new method of segmenting unconstrained handwritten numeral strings is proposed. It is based on the extracting of foreground and background features. In order to find foreground features for the first time an algorithm based on skeleton tracing is introduced. The skeleton of each connected component is traversed in clockwise and anti-clockwise directions, and intersection points which are visited in each traversal, are mapped on the outer contour to form foreground feature points. In order to find background features, another new algorithm is proposed. Considering vertical projections of top and bottom profiles, two background skeletons are found. After processing these two background skeletons, background feature points are extracted. Background and foreground feature points are assigned together to construct candidate segmentation paths. Finally each segmentation path is evaluated based on the properties of its left and right connected components. Our method can provide a list of good segmentation hypotheses for segmentation-based recognition systems. The NIST SD19 database (handwritten numeral strings) is used for evaluating of the method, and experiments show a very promising result.
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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 | 0.001 |
| 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 it