{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003502233,0.0004153237,0.0004989592,0.001036113,0.000269296,0.0006471506,0.0007330334,0.000522094,0.002472571],"category_scores_gemma":[0.0008940059,0.0002331925,0.0004136267,0.001046799,0.0002554055,0.001076829,0.0003198512,0.0004759693,0.0008536726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004325201,"about_ca_system_score_gemma":0.0004533073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004177094,"about_ca_topic_score_gemma":0.003165421,"domain_scores_codex":[0.9996459,0.00003588341,0.0000274502,0.0001018393,0.0001570773,0.00003182628],"domain_scores_gemma":[0.9996929,0.00006121113,0.00003337734,0.0000358327,0.0001637389,0.00001296504],"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":[0.0002853828,0.0001223957,0.001675768,0.0001375058,0.00007455006,0.0001170572,0.00007405559,0.03512959,0.05852588,0.002502506,0.002418406,0.8989369],"study_design_scores_gemma":[0.00001622521,0.0001295617,0.00363139,0.00002867325,0.00004100519,0.0002786447,0.00004264763,0.9386084,0.05093847,0.0018206,0.004430246,0.00003418533],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08220452,0.001293716,0.9049016,0.0002023853,0.000227408,0.0002165578,0.0001886702,0.003245267,0.007519901],"genre_scores_gemma":[0.5188048,0.0009568396,0.4654538,0.0001150568,0.00008459516,0.0001633506,0.0005166346,0.0001311398,0.01377377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004177094,"threshold_uncertainty_score":0.00830555,"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."}}