{"id":"W2144026429","doi":"10.1109/icdar.2001.953836","title":"Character recognition experiments using Unipen data","year":2002,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backpropagation; Computer science; Pattern recognition (psychology); Handwriting; Artificial intelligence; Cursive; Classifier (UML); Character recognition; Handwriting recognition; Artificial neural network; Speech recognition; Training set; Representation (politics); Optical character recognition; Feature extraction","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001675269,0.001245581,0.001222102,0.001627869,0.0007390921,0.0007252966,0.001643853,0.001317951,0.006046121],"category_scores_gemma":[0.00751615,0.0002802583,0.0006119687,0.002120749,0.0004639777,0.001223171,0.0009002618,0.0008973667,0.002647866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004301329,"about_ca_system_score_gemma":0.000210841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003651867,"about_ca_topic_score_gemma":0.003713449,"domain_scores_codex":[0.9975474,0.0005004703,0.0004086861,0.0006852247,0.0006438165,0.0002143084],"domain_scores_gemma":[0.9948836,0.002093659,0.0001975236,0.001163618,0.001443325,0.000218272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007711342,0.004642004,0.01109419,0.002554474,0.0007593921,0.00220637,0.0008728137,0.0614,0.1128764,0.001317867,0.0284747,0.7660905],"study_design_scores_gemma":[0.00104106,0.009576458,0.06124619,0.0001748102,0.0005019004,0.00531884,0.001554269,0.3860196,0.4971911,0.001683429,0.03533461,0.0003576519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9610756,0.001090203,0.01764661,0.0002391632,0.0003168252,0.0005997163,0.006209559,0.00374827,0.009074093],"genre_scores_gemma":[0.8724936,0.0007206933,0.08064292,0.0002427153,0.000122134,0.0005489076,0.03464574,0.0004435699,0.01013972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006046121,"threshold_uncertainty_score":0.0202263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2512199841090454,"score_gpt":0.332919803090206,"score_spread":0.08169981898116058,"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."}}