{"id":"W2943378958","doi":"10.5539/cis.v12n2p126","title":"Arabic Hand Written Character Recognition Based on Contour Matching and Neural Network","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Character (mathematics); Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Set (abstract data type); Arabic; Font; Matching (statistics); Intelligent character recognition; Word (group theory); Character recognition; Speech recognition; Natural language processing; Image (mathematics); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008139084,0.0001428247,0.0001515307,0.0003152462,0.0003386199,0.001524967,0.0003945124,0.0000497199,0.00001729308],"category_scores_gemma":[0.00001698822,0.0001234594,0.00002637218,0.0004832906,0.0001598806,0.009904249,0.0002117637,0.0001499024,0.0001027733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000259114,"about_ca_system_score_gemma":0.0000531493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005446682,"about_ca_topic_score_gemma":3.620166e-7,"domain_scores_codex":[0.9987125,0.00003950835,0.000294364,0.0002897241,0.0003745992,0.0002892702],"domain_scores_gemma":[0.9991214,0.0001038588,0.0001459458,0.0002747229,0.0002206821,0.0001334021],"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":[0.00001460061,0.00001744244,0.001250602,0.00004402834,0.000002763043,0.000001229902,0.0009561717,0.0003384513,0.0004567282,0.004753316,0.0003397291,0.9918249],"study_design_scores_gemma":[0.0005926445,0.0002832428,0.03769512,0.0001415347,0.000002478537,0.00004099136,0.000015928,0.9545876,0.001677932,0.002353718,0.002322375,0.0002864241],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3296755,0.000009305775,0.6672936,0.0005522539,0.0003975111,0.0002892732,0.000003057352,0.0001667479,0.001612756],"genre_scores_gemma":[0.9527842,0.00001568274,0.03978069,0.007292479,0.00008586754,0.00001371303,0.00001105564,0.000003451599,0.00001286447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9915385,"threshold_uncertainty_score":0.9995115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252677328415966,"score_gpt":0.2283319288556304,"score_spread":0.2158051555714707,"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."}}