{"id":"W2118845658","doi":"10.1109/icdar.2005.23","title":"A new method of recognizing Chinese fonts","year":2005,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Hilbert–Huang transform; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Feature (linguistics); Feature vector; Feature extraction; Chinese characters; Speech recognition; Mode (computer interface); Computer vision","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":[0.000409066,0.0006790157,0.0006998569,0.00176855,0.0003838309,0.0008927257,0.0006937922,0.0005122186,0.003856082],"category_scores_gemma":[0.000971503,0.0003239364,0.0005347805,0.0009427686,0.0004110438,0.001271191,0.0006220918,0.0006985955,0.002436168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002809782,"about_ca_system_score_gemma":0.0004460723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009283601,"about_ca_topic_score_gemma":0.001344123,"domain_scores_codex":[0.9994179,0.00004470286,0.00003461506,0.0001790633,0.0002843633,0.00003925427],"domain_scores_gemma":[0.9994247,0.00007519192,0.00003687563,0.0001029992,0.0003161037,0.00004407113],"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":[0.0001319158,0.00005324616,0.001495921,0.0001897433,0.00005347883,0.0001865546,0.0001461205,0.00229692,0.2002853,0.00507834,0.006196038,0.7838864],"study_design_scores_gemma":[0.00009897036,0.0003765209,0.01748274,0.0001088741,0.0001465376,0.005823555,0.0002456055,0.5016407,0.3308247,0.007547328,0.135477,0.0002273914],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01754132,0.0007007968,0.9742717,0.00009940672,0.0003351191,0.00008713979,0.0002002752,0.002370403,0.004393792],"genre_scores_gemma":[0.09061617,0.0006268292,0.8916151,0.00008693946,0.0001314917,0.0001278643,0.0005024265,0.0002019735,0.0160912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003856082,"threshold_uncertainty_score":0.01289994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999840545776673,"score_gpt":0.3218083564132845,"score_spread":0.3018099509555178,"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."}}