MétaCan
Menu
Back to cohort
Record W1906553149 · doi:10.1109/icpr.1998.711998

HMM-KNN word recognition engine for bank cheque processing

2002· article· en· W1906553149 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceHidden Markov modelChequeClassifier (UML)PhoneWord (group theory)AlphabetSpeech recognitionArtificial intelligenceNatural language processingSegmentationModular designDocument processingScheme (mathematics)Set (abstract data type)Pattern recognition (psychology)Linguistics

Abstract

fetched live from OpenAlex

Describes the mixed HMM-KNN word recognition module of a bank cheque processing system developed at CENPARMI. It uses a combination of 2 segmentation free word recognition schemes. The first scheme uses a set of global features associated to a modified K nearest neighbour classifier; while the second one uses a set of directional contour features as input to an HMM. The system has been designed to be modular and independent of specific languages as in Canada one has to deal with at least 2 languages, namely English and French. It can be easily adapted to read other European languages based on the Roman alphabet. The system is continuously tested on data from the local phone company, and we report here the results on a database of approximately 4,500 cheques.

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: Methods
Teacher disagreement score0.995
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.245
Teacher spread0.174 · 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

Citations41
Published2002
Admission routes2
Has abstractyes

Explore more

Same topicSpeech Recognition and SynthesisFrench-language works237,207