A Comparative Survey of Image Binarisation Algorithms for Optical Recognition on Degraded Musical Sources.
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
Binarisation of greyscale images is a critical step in optical music recognition (OMR) preprocessing. Binarising music documents is particularly challenging because of the nature of music notation, even more so when the sources are degraded, e.g., with ink bleed-through from the other side of the page. This paper presents a comparative evaluation of 25 binarisation algorithms tested on a set of 100 music pages. A real-world OMR infrastructure for early music (Aruspix) was used to perform an objective, goaldirected evaluation of the algorithms’ performance. Our results differ significantly from the ones obtained in studies on non-music documents, which highlights the importance of developing tools specific to our community.
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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 | 0.001 | 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 | 0.003 |
| 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