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Record W2148667307 · doi:10.1109/icdar.1997.619872

Piecewise linear modulation model of handwriting

2002· article· en· W2148667307 on OpenAlex
Hao Chen, O.E. Agazzi, Ching Y. Suen

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHandwritten Text Recognition Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsHandwritingImpulse responsePiecewise linear functionComputer sciencePiecewiseTrajectoryComputationAlgorithmControl theory (sociology)MathematicsArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

A new piecewise linear modulation model of handwriting is proposed. In this model, the velocity of handwriting trajectory is modeled as the impulse response of a time varying second order system. For mathematical tractability, the entire trajectory is segmented into several non-overlapping frames, while the natural frequency and the damping factor of the system are assumed to vary linearly with time in each frame and are continuous along the entire trajectory. In other words, handwriting is regarded as an oscillation modulated by a continuous and piecewise linear signal. The parameters of this model are estimated by Powell's optimization algorithm which does not require the computation of the first order derivative. The number and lengths of the frames are decided by a modified binary search algorithm along with the estimation of parameters. This model has achieved very high data compression rate as well as accurate reproduction of real handwriting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.254
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations12
Published2002
Admission routes1
Has abstractyes

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