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Record W2131877510 · doi:10.1109/icdar.1995.598933

Cursive script recognition applied to the processing of bank cheques

2002· article· en· W2131877510 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHandwritten Text Recognition Techniques
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Standards and Technology
KeywordsCursiveComputer scienceLexiconHandwritingSpeech recognitionNatural language processingProcess (computing)Domain (mathematical analysis)Handwriting recognitionArtificial intelligenceReading (process)SegmentationFeature extractionLinguisticsProgramming languageMathematics

Abstract

fetched live from OpenAlex

A method for recognizing unconstrained handwritten words belonging to a small static lexicon is proposed. Our computational theory is based on a psychological model of the reading process of a fast reader. The method we propose is global in its nature and avoid the difficult segmentation stage of common word recognition techniques. Our computational theory has been applied to the processing of handwritten bank cheques, whose problem domain is that of unconstrained handwriting, unlimited writers in a small static lexicon. Current results seem comparable to those published in the literature and support our computational theory.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.248
Teacher spread0.211 · 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

Citations53
Published2002
Admission routes2
Has abstractyes

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