MétaCan
Menu
Back to cohort
Record W2160689653 · doi:10.1109/icdar.1997.620589

Shape matrices as a mixed shape factor for off-line signature verification

2002· article· en· W2160689653 on OpenAlex

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 institutionsnot available
Fundersnot available
KeywordsShape factorSignature (topology)Shape analysis (program analysis)Similarity (geometry)Representation (politics)Measure (data warehouse)Heat kernel signatureActive shape modelFactor (programming language)Interpretation (philosophy)MathematicsLine (geometry)Position (finance)Pattern recognition (psychology)Artificial intelligencePlanarComputer scienceGeometryData miningImage (mathematics)Segmentation

Abstract

fetched live from OpenAlex

Shape matrices have been used as a representation of planar shapes like industrial parts or printed characters. We investigate the use of shape matrices as a mixed shape factor for offline signature verification. By mixed shape factor we mean any global shape factor where the position of local measurements are taken into account in the definition of a similarity measure between two representations. It is demonstrated that when using a good similarity measure between two shape matrices, this shape factor is relatively well suited for the global interpretation of signature images.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.274
Teacher spread0.238 · 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

Citations30
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicHandwritten Text Recognition TechniquesFrench-language works237,207