{"id":"W2315062801","doi":"10.5121/csit.2014.4219","title":"Off-Line System for the Recognition of Handwritten Arabic Character","year":2014,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Character (mathematics); Artificial intelligence; Intelligent word recognition; Intelligent character recognition; Arabic; Preprocessor; Handwriting recognition; Speech recognition; Handwriting; Optical character recognition; Pattern recognition (psychology); Character recognition; Feature extraction; Word (group theory); Variation (astronomy); Natural language processing; Line (geometry); Image (mathematics); Mathematics; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000913877,0.0001193291,0.0002012792,0.00009943543,0.0001050825,0.00008261173,0.0006056548,0.00007283284,0.0000289617],"category_scores_gemma":[0.0001025412,0.00007589107,0.0001205211,0.0001922186,0.00004458213,0.0003217865,0.00008683604,0.00007747028,0.00007936106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002147803,"about_ca_system_score_gemma":0.00002046006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001694529,"about_ca_topic_score_gemma":0.000007343443,"domain_scores_codex":[0.9989257,0.00007975825,0.0003487099,0.0002612561,0.0001904031,0.0001941857],"domain_scores_gemma":[0.9984533,0.0004832752,0.0001711558,0.0004944372,0.0003513359,0.00004651835],"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":[0.00001253257,0.00004541086,0.00003103078,0.000105484,0.00002461873,3.115659e-7,0.00008228633,5.630595e-7,0.004463207,0.01599391,0.001490718,0.9777499],"study_design_scores_gemma":[0.001380148,0.0006905238,0.001408738,0.0003621723,0.00006881677,0.00006352679,0.0001005457,0.1542756,0.7928593,0.01550311,0.03278636,0.0005011332],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004756595,0.0000439117,0.989105,0.001332479,0.000248961,0.0005608114,0.00001153242,0.0004452094,0.003495508],"genre_scores_gemma":[0.9344491,0.00002935171,0.06386688,0.0006448306,0.0002198208,0.0002688112,0.00001691215,0.00001437514,0.0004898901],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9772488,"threshold_uncertainty_score":0.3094748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02582771084042468,"score_gpt":0.2490143021077125,"score_spread":0.2231865912672878,"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."}}