Pen Acoustic Emissions for Text and Gesture Recognition
Bibliographic record
Abstract
The sounds generated by a writing instrument provide a rich and under-utilized source of information for pattern recognition. We examine the feasibility of recognition of handwritten cursive text, exclusively through an analysis of acoustic emissions. Our recognizer uses a template matching approach, with templates and similarity measures derived variously from: raw power signal with fixed resolution, discrete sequence of magnitudes obtained from peaks in the power signal, and ordered tree obtained from a scale space signal representation. Test results are presented for isolated lowercase cursive characters and for whole words. Recognition rates of over 70% (alphabet) and 90% (26 words) are achieved, based solely on acoustic emissions, with samples provided by a single writer. We also present qualitative results for recognizing gestures such as circling, scratch-out, check-marks, and hatching. These preliminary results demonstrate that acoustic emissions are a rich source of information, usable - on their own or in conjunction with image-based featuresi - to solve pattern recognition problems. In future work, this approach can be used in applications such as writer identification, handwriting and gesture-based computer input technology, emotion recognition, and temporal analysis of sketches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".