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Record W2057340122 · doi:10.1109/icpr.2010.496

A Novel Handwritten Urdu Word Spotting Based on Connected Components Analysis

2010· article· en· W2057340122 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 institutionsConcordia University
Fundersnot available
KeywordsSpottingUrduComputer scienceArtificial intelligenceWord (group theory)Keyword spottingSpeech recognitionWord recognitionNatural language processingRecall ratePattern recognition (psychology)Word error rateMathematicsReading (process)

Abstract

fetched live from OpenAlex

We propose a novel word spotting system for Urdu words within handwritten text lines. Spatial information of diacritics is integrated to the detection of the main connected components in candidate words generation. An Urdu word recognition system is effectively designed and applied to classify the candidate words. In this word recognition system, compound features and SVM were adapted. The verification/rejection process was based on the outputs from the Urdu word recognition system and the image's global features were applied to achieve a promising result. As a result, a high 92.11% correct segmentation rate, a 50.75% word spotting precision rate were achieved while maintaining a 70.1% recall on CENPARMI's Urdu Database.

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)0.001
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 score0.839
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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