{"id":"W4392354806","doi":"10.18280/ria.380101","title":"Data Augmentation for Offline Arabic Handwritten Text Recognition Using Moving Least Squares","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Handwriting; Artificial intelligence; Convolutional neural network; Task (project management); Deep learning; Handwriting recognition; Arabic; Generative grammar; Natural language processing; Artificial neural network; Speech recognition; Pattern recognition (psychology); Feature extraction; Linguistics","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":[0.0005684707,0.00101944,0.0007223616,0.0006269451,0.0002965873,0.0005566603,0.0008129746,0.0006918088,0.004463313],"category_scores_gemma":[0.002257537,0.0003787143,0.0008290736,0.0006063744,0.0004248603,0.001102249,0.0006112028,0.001496815,0.00238191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004277645,"about_ca_system_score_gemma":0.0004744269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002824858,"about_ca_topic_score_gemma":0.004172752,"domain_scores_codex":[0.9994528,0.00008146992,0.00004251446,0.0001870467,0.0001951758,0.00004104124],"domain_scores_gemma":[0.998934,0.0004335429,0.0001238721,0.000201549,0.0002685697,0.00003854172],"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":[0.0004011618,0.0001436216,0.0008911059,0.0001874691,0.00007611641,0.0002649167,0.0001174929,0.1193645,0.1028837,0.001280242,0.004549599,0.76984],"study_design_scores_gemma":[0.00001385332,0.0001371328,0.0009918649,0.00001712403,0.00001486106,0.0001188288,0.00003566463,0.9388266,0.05452224,0.0009646051,0.004332373,0.00002488188],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04704684,0.000600324,0.9421991,0.0002753066,0.0002101946,0.000110891,0.0003906692,0.006938434,0.002228213],"genre_scores_gemma":[0.3371854,0.0003366397,0.6534829,0.0002703679,0.00009110425,0.0001638471,0.001450063,0.0004869156,0.006532846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004463313,"threshold_uncertainty_score":0.01493126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.137066026589948,"score_gpt":0.347834769432749,"score_spread":0.210768742842801,"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."}}