{"id":"W2144108697","doi":"10.1109/mwscas.2011.6026438","title":"Online Arabic/Persian character recognition using neural network classifier and DCT features","year":2011,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Cursive; Computer science; Intelligent character recognition; Handwriting recognition; Artificial intelligence; Character recognition; Handwriting; Classifier (UML); Arabic; Character (mathematics); Pattern recognition (psychology); Feature extraction; Speech recognition; Artificial neural network; Intelligent word recognition; Persian; Set (abstract data type); Arabic script; Feature (linguistics); Optical character recognition; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001863473,0.0001772464,0.0001730112,0.000119793,0.0001419545,0.0001427365,0.0002921428,0.0001268037,0.0001968647],"category_scores_gemma":[0.00001726433,0.000151085,0.00006298401,0.00024976,0.00006709084,0.0009121444,0.0001636581,0.0002246119,0.00002616757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002276676,"about_ca_system_score_gemma":0.00002078063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007540118,"about_ca_topic_score_gemma":0.00004684992,"domain_scores_codex":[0.9988135,0.00008846093,0.0002160512,0.0004050102,0.0001478186,0.0003291499],"domain_scores_gemma":[0.9993504,0.0000353869,0.00009197029,0.0002868193,0.0001070322,0.0001284097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003456882,0.0002723156,0.003234988,0.00002704724,0.00004988735,0.00006454314,0.001081773,0.000001547715,0.001947986,0.002232384,0.00420812,0.9868448],"study_design_scores_gemma":[0.002969422,0.001623165,0.5482702,0.0007817868,0.0002215597,0.002788104,0.000662409,0.2484122,0.06837189,0.1138077,0.007663784,0.00442782],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7668701,0.0001761093,0.2113379,0.0009431537,0.0006979852,0.0005311056,0.00001688858,0.001359381,0.01806736],"genre_scores_gemma":[0.5985599,0.00005396098,0.3980481,0.002602368,0.0003426306,0.00001259064,0.00002605796,0.00002032662,0.0003340013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.982417,"threshold_uncertainty_score":0.6161066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0769545768451445,"score_gpt":0.2648684599243065,"score_spread":0.187913883079162,"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."}}