{"id":"W3004984598","doi":"10.1109/icivc47709.2019.8981053","title":"Chinese Character Recognition by Pseudo-Zernike Moments","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Zernike polynomials; Velocity Moments; Pattern recognition (psychology); Character (mathematics); Character recognition; Artificial intelligence; Invariant (physics); Moment (physics); Feature extraction; Computer science; Statistic; Feature (linguistics); Feature vector; Computer vision; Mathematics; Image (mathematics); Physics; Geometry; Statistics; Optics","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":[0.0002820134,0.0004575498,0.0004629911,0.001990616,0.0002412931,0.0005775925,0.0004018882,0.0002994113,0.001599744],"category_scores_gemma":[0.00136025,0.000171437,0.0005013116,0.00166537,0.0003870339,0.001095697,0.0003207946,0.0004267168,0.0007494182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003292227,"about_ca_system_score_gemma":0.0003065992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002069627,"about_ca_topic_score_gemma":0.002273401,"domain_scores_codex":[0.9995322,0.00006072218,0.00002941515,0.00007476574,0.0002492386,0.00005360907],"domain_scores_gemma":[0.9995166,0.0001162989,0.00008732382,0.00006285777,0.0001910053,0.00002585303],"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":[0.0003302884,0.00005961048,0.002906702,0.0002388088,0.00007346262,0.0002730154,0.0001295123,0.01543029,0.2412028,0.00614919,0.003931159,0.7292752],"study_design_scores_gemma":[0.00004468896,0.000446523,0.02966976,0.00004782593,0.0001441423,0.002276318,0.0002044661,0.6411816,0.2953078,0.006160194,0.0243109,0.0002057551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1540318,0.001531277,0.8361198,0.0002198344,0.0002162311,0.0001313589,0.0004851707,0.002460981,0.004803611],"genre_scores_gemma":[0.6914349,0.000932942,0.3023415,0.00006859139,0.000116487,0.00007885483,0.0008392744,0.00012073,0.004066784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002069627,"threshold_uncertainty_score":0.005351603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009271524487368146,"score_gpt":0.2462500829991909,"score_spread":0.2369785585118228,"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."}}