Towards Preserving the Ephemeral: Texture-Based Background Modelling for Capturing Back-of-the-Napkin Notes
Bibliographic record
Abstract
A back-of-the-napkin idea is typically created on the spur of the moment and captured via a few hand-sketched notes on whatever material is available, which often happens to be an actual paper napkin. This paper explores the preservation of such back-of-the-napkin ideas. Hand-sketched notes, reflecting those flashes of inspiration, are not limited to text; they can also include drawings and graphics. Napkin backgrounds typically exhibit diverse textural and colour motifs/patterns that may have high visual saliency from a low-level vision standpoint. We thus frame the extraction of hand-sketched notes as a background modelling and removal task. We propose a novel document background model based on texture mixtures constructed from the document itself via texture synthesis, which allows us to identify background pixels and extract hand-sketched data as foreground elements. Experiments on a novel napkin image dataset yield excellent results and showcase the robustness of our method with respect to the napkin contents. A texture-based background modelling approach, such as ours, is generic enough to cope with any type of hand-sketched notes.
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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 | 0.000 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".