{"id":"W3196382033","doi":"10.1007/978-3-030-86198-8_23","title":"Improving Handwritten Arabic Text Recognition Using an Adaptive Data-Augmentation Algorithm","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cambrian College","funders":"","keywords":"Computer science; Lexicon; Word (group theory); Artificial intelligence; Arabic; Natural language processing; Task (project management); Pattern recognition (psychology); Handwriting recognition; Speech recognition; Feature extraction; 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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001576932,0.0006903521,0.0006713737,0.001170881,0.0004608816,0.001456489,0.003918973,0.0004747356,0.00008789829],"category_scores_gemma":[0.000126359,0.0007215475,0.0001237191,0.0008985354,0.0005195317,0.004215309,0.00249422,0.001011782,0.00004497097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005937836,"about_ca_system_score_gemma":0.001025057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001815765,"about_ca_topic_score_gemma":0.0001581279,"domain_scores_codex":[0.9940699,0.0001519426,0.0008192362,0.002937907,0.001271339,0.0007496378],"domain_scores_gemma":[0.9954904,0.000323495,0.0006160729,0.002490414,0.0008183093,0.000261311],"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":[0.000004140428,0.00005230809,0.000001772586,0.00002325901,0.0000147759,0.0001142709,0.0001915862,0.000384158,0.0009264422,0.0002159491,0.000005221375,0.9980661],"study_design_scores_gemma":[0.0003219264,0.0003120606,0.00001130319,0.0006381971,0.00003597095,0.0002661219,0.000001634876,0.9307927,0.01395186,0.05262372,0.0001014204,0.0009431154],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001070677,0.0003835356,0.9965995,0.0001319868,0.001114441,0.0006621979,0.00009380352,0.000435297,0.0004721715],"genre_scores_gemma":[0.005212485,0.00006527121,0.9924878,0.001027302,0.000690711,0.00001918401,0.0003453043,0.00006310676,0.00008877905],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.997123,"threshold_uncertainty_score":0.9995801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06282409887958935,"score_gpt":0.2912863128797311,"score_spread":0.2284622140001417,"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."}}