{"id":"W2917150894","doi":"10.1109/icdar.2011.287","title":"ICDAR 2011 - Arabic Handwriting Recognition Competition","year":2011,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Sfax; University of Jordan; Concordia University; University of Sharjah","keywords":"Arabic; Handwriting; Competition (biology); Handwriting recognition; Computer science; Artificial intelligence; Text recognition; Speech recognition; Natural language processing; Feature extraction; Linguistics; Image (mathematics); Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.008168243,0.002742133,0.002940884,0.004196388,0.00216091,0.003620086,0.002923526,0.003031186,0.01741835],"category_scores_gemma":[0.007470267,0.0004028966,0.001319052,0.002599486,0.0005847884,0.002303117,0.002328493,0.00262121,0.0219423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001901061,"about_ca_system_score_gemma":0.002697266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01223858,"about_ca_topic_score_gemma":0.01948016,"domain_scores_codex":[0.9942771,0.0009562748,0.0005378473,0.0007918385,0.002732234,0.0007046926],"domain_scores_gemma":[0.9931145,0.000776512,0.0001667513,0.0009515481,0.003630883,0.001359807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009753107,0.0006999778,0.001089948,0.0005156408,0.0001149867,0.000519592,0.00009816097,0.002643958,0.009609237,0.00158296,0.7209541,0.2611961],"study_design_scores_gemma":[0.00054966,0.001080003,0.01176567,0.0001340322,0.00009441649,0.001831319,0.0004228044,0.04269601,0.04037416,0.002755399,0.8981217,0.0001749728],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2146869,0.04352106,0.2255632,0.0300795,0.05747667,0.008292134,0.1677223,0.05771144,0.1949468],"genre_scores_gemma":[0.1129405,0.006844352,0.1236315,0.004323229,0.003675202,0.001461108,0.4806292,0.002958764,0.2635362],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01741835,"threshold_uncertainty_score":0.05827022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05016178984653617,"score_gpt":0.2380046570476502,"score_spread":0.187842867201114,"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."}}