{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000258403,0.0004848091,0.0005480878,0.0008145347,0.0002617882,0.0004478266,0.0004284825,0.0004834169,0.002759535],"category_scores_gemma":[0.0006438178,0.0001493687,0.000267896,0.0006212406,0.0001556003,0.0008073281,0.000209216,0.0004895138,0.00125763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003068846,"about_ca_system_score_gemma":0.0003521151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004107276,"about_ca_topic_score_gemma":0.005586912,"domain_scores_codex":[0.9997572,0.00003217132,0.00001888338,0.00005944733,0.00009976369,0.00003262369],"domain_scores_gemma":[0.9996378,0.00009601162,0.00003698413,0.0000366207,0.0001703008,0.00002228545],"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":[0.0003180856,0.00018888,0.001571105,0.00009382267,0.00004478166,0.0001841825,0.00002675168,0.01351707,0.1010092,0.00052351,0.003064297,0.8794582],"study_design_scores_gemma":[0.00003234835,0.0002761054,0.005014337,0.00002082175,0.00005141234,0.0003473805,0.00004308327,0.9115947,0.07823143,0.0004642048,0.003895066,0.00002911123],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2239935,0.001694976,0.7548324,0.000300019,0.0003694476,0.0002000756,0.0003760887,0.006120664,0.01211285],"genre_scores_gemma":[0.678494,0.000694023,0.3046481,0.0001060707,0.0001276372,0.0001076997,0.0007831571,0.00008527791,0.01495398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004107276,"threshold_uncertainty_score":0.009231627,"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."}}