{"id":"W2118923639","doi":"10.1109/icdar.2011.195","title":"Discovering Legible Chinese Typefaces for Reading Digital Documents","year":2011,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Typeface; Legibility; Reading (process); Computer science; Character (mathematics); Typography; Font; Multimedia; Computer graphics (images); Artificial intelligence; Visual arts; Art; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001344769,0.0005381443,0.0003316322,0.002111795,0.0003853284,0.001381305,0.0002980369,0.0003782982,0.001687334],"category_scores_gemma":[0.01145506,0.0001510216,0.0003003451,0.001380957,0.00032849,0.001994118,0.0003663716,0.0003353342,0.000622302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003321277,"about_ca_system_score_gemma":0.0004177801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008388142,"about_ca_topic_score_gemma":0.001476349,"domain_scores_codex":[0.9989967,0.000385457,0.0001053253,0.0002039648,0.0002560362,0.00005257919],"domain_scores_gemma":[0.994509,0.002688149,0.0009171854,0.0004850845,0.001144544,0.0002560632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006877006,0.000275245,0.08489241,0.0006188762,0.00005624369,0.0004078588,0.003260339,0.00164909,0.1474437,0.001201794,0.001387944,0.7581189],"study_design_scores_gemma":[0.000142787,0.003593019,0.6139722,0.0003443925,0.0003995621,0.003597047,0.009103594,0.07913212,0.2659312,0.007618887,0.01584731,0.0003177265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9270556,0.000590079,0.06779952,0.0001248444,0.00002971389,0.0002148077,0.0002260653,0.0004395432,0.003519912],"genre_scores_gemma":[0.9030403,0.0003945282,0.09498326,0.00004188272,0.00002540708,0.00009083833,0.0002538098,0.00005438065,0.001115549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002111795,"threshold_uncertainty_score":0.007111907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02344135469514739,"score_gpt":0.2690134176462312,"score_spread":0.2455720629510838,"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."}}