Pen Acoustic Emissions for Text and Gesture Recognition
Why this work is in the frame
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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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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 | 0.000 |
| 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