{"id":"W3108615237","doi":"10.1504/ijapr.2020.10033768","title":"Arabic literal amount sub-word recognition using multiple features and classifiers","year":2020,"lang":"en","type":"article","venue":"International Journal of Applied Pattern Recognition","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Fuels Association","funders":"","keywords":"Computer science; Artificial intelligence; Literal (mathematical logic); Arabic; Support vector machine; Decision tree; Classifier (UML); Pattern recognition (psychology); Word (group theory); Natural language processing; Arabic numerals; Artificial neural network; Speech recognition; Mathematics","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":[0.0004706835,0.0006602143,0.0008529075,0.002172701,0.0003096936,0.0008015548,0.0005247676,0.0007174736,0.001886668],"category_scores_gemma":[0.001325391,0.0001835156,0.0005895818,0.001389097,0.0001871002,0.001685274,0.0004271047,0.0004966162,0.001967635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002253078,"about_ca_system_score_gemma":0.000308217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001720048,"about_ca_topic_score_gemma":0.002352133,"domain_scores_codex":[0.9994107,0.00006508004,0.00006378976,0.0001279041,0.0002632303,0.00006933649],"domain_scores_gemma":[0.9992707,0.0001917197,0.00009817669,0.00008073245,0.0003219937,0.00003666419],"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":[0.0004532592,0.0001664399,0.005746922,0.0001895148,0.00009707981,0.0003625235,0.00007187103,0.003402216,0.131154,0.0005138885,0.003960841,0.8538814],"study_design_scores_gemma":[0.00005888999,0.0008982021,0.05581509,0.0001268892,0.0003124035,0.002502541,0.000615632,0.644663,0.2752873,0.003171239,0.01641604,0.0001327114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4270465,0.002813243,0.5528347,0.0004579204,0.0004020917,0.0003009328,0.001256544,0.007092576,0.007795393],"genre_scores_gemma":[0.7380871,0.0007084135,0.2539164,0.0001090591,0.0001350583,0.00009864391,0.001763802,0.0001043587,0.005077197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002172701,"threshold_uncertainty_score":0.006311536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0412532024615218,"score_gpt":0.2661508901359665,"score_spread":0.2248976876744447,"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."}}