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Record W4379417306 · doi:10.14209/sbrt.2000.5250251

Handwritten Numerical Characters Recognition Using Biorthogonal Spline Wavelets

2000· article· pt· W4379417306 on OpenAlexaboutno aff
Suzete Correia, J.M. de Carvalho

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldComputer Science
TopicHandwritten Text Recognition Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBiorthogonal systemWaveletSpline (mechanical)Computer scienceArtificial intelligencePattern recognition (psychology)Speech recognitionWavelet transformStructural engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, a novel approach for recognition of handwritten numerical characters using the biorthogonal spline wavelets Cohen-Daubechies-Feauveau (CDF) 3/7 is proposed.The main purpose is to optmize character recognition methods and develop a system which is able to eciently absorb the variations present e d i n t h i s t ype of data.The proposed scheme consists of three stages: scale normalization, feature extraction and classi cation.The classi er used is a multilayer cluster neural network.In order to verify the performance of the method, experiments were realized with the numerical database of Concordia University of Canada, obtaining an recognition rate of 94.7 %.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0240.004

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.

Opus teacher head0.044
GPT teacher head0.280
Teacher spread0.237 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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