{"id":"W2057340122","doi":"10.1109/icpr.2010.496","title":"A Novel Handwritten Urdu Word Spotting Based on Connected Components Analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Spotting; Urdu; Computer science; Artificial intelligence; Word (group theory); Keyword spotting; Speech recognition; Word recognition; Natural language processing; Recall rate; Pattern recognition (psychology); Word error rate; Mathematics; Reading (process)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005312068,0.0002277557,0.0003310809,0.0007967441,0.0001840617,0.0003186427,0.0009816246,0.0001388713,0.0004838569],"category_scores_gemma":[0.0001673135,0.0002020491,0.0002252861,0.00187639,0.00005802049,0.0003142422,0.0001612562,0.0003910611,0.0001555478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002715006,"about_ca_system_score_gemma":0.00004599304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000177644,"about_ca_topic_score_gemma":0.0001769845,"domain_scores_codex":[0.9981384,0.00005055968,0.0003581148,0.0006160358,0.0004572989,0.0003796307],"domain_scores_gemma":[0.9981948,0.0003395836,0.0001416232,0.0009065888,0.0002280816,0.0001893763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000706041,0.001942806,0.01941988,0.00003577726,0.0006680134,0.00007988077,0.0003447519,0.0001880375,0.7032278,0.03145307,0.002524215,0.2400452],"study_design_scores_gemma":[0.001149925,0.0001091372,0.05002163,0.00003658342,0.0001109915,0.00001315308,0.00001245521,0.8394275,0.1056861,0.0008469179,0.001989376,0.0005962519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1450852,0.000001409491,0.8439272,0.001097582,0.0001590534,0.0002096486,0.000008109907,0.001239743,0.008271988],"genre_scores_gemma":[0.7151486,4.278526e-7,0.2830565,0.001476345,0.00004057139,0.00002936028,0.00002457332,0.00001101005,0.0002126699],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8392395,"threshold_uncertainty_score":0.8239322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01797248907648489,"score_gpt":0.2516692729215714,"score_spread":0.2336967838450865,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}