{"id":"W2022493994","doi":"10.1109/icalip.2014.7009883","title":"Chinese characters recognition via Racah moments","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Moment (physics); Feature (linguistics); Pattern recognition (psychology); Computer science; Artificial intelligence; Character recognition; Character (mathematics); Velocity Moments; Image moment; Set (abstract data type); Feature extraction; Feature vector; Cognitive neuroscience of visual object recognition; Field (mathematics); Computer vision; Image (mathematics); Mathematics; Image processing; Zernike polynomials; Physics; Geometry; 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":[0.0002423536,0.0005131481,0.0006131866,0.002021925,0.0003427088,0.0006696308,0.0004073008,0.000334068,0.002079028],"category_scores_gemma":[0.00126398,0.0001944192,0.0004459235,0.001812847,0.0004272857,0.001229117,0.0005397993,0.0004650528,0.001026733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003174903,"about_ca_system_score_gemma":0.0003178353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001895315,"about_ca_topic_score_gemma":0.00169607,"domain_scores_codex":[0.9994838,0.00007155806,0.00003138848,0.00009448699,0.0002411552,0.00007755784],"domain_scores_gemma":[0.9995849,0.00007226835,0.00008295799,0.00007035377,0.0001608584,0.00002868116],"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":[0.0007162489,0.00006886046,0.002476578,0.0001930948,0.00007432006,0.0004260607,0.0002044189,0.01948287,0.1687858,0.01094642,0.006349646,0.7902756],"study_design_scores_gemma":[0.00004685589,0.0004538564,0.0137513,0.0000493154,0.0001237934,0.001900609,0.0002233624,0.7220353,0.2287107,0.008973756,0.02355004,0.0001810765],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2216146,0.001420172,0.7602186,0.0003307703,0.0003342789,0.0001752486,0.0007624413,0.003579936,0.01156406],"genre_scores_gemma":[0.8555638,0.0008684793,0.1362503,0.0001174523,0.0001455789,0.00008577736,0.001019102,0.00009938105,0.005850253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002079028,"threshold_uncertainty_score":0.006955087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177742117773065,"score_gpt":0.2511430585707634,"score_spread":0.2393656373930328,"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."}}