{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000387848,0.0001546107,0.0001528755,0.0002113508,0.000151463,0.0001085584,0.00048767,0.00009441921,0.002456648],"category_scores_gemma":[0.00003036059,0.0001498112,0.00008213522,0.0002103493,0.00005377906,0.001170072,0.0001352939,0.0001514724,0.002907568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003331572,"about_ca_system_score_gemma":0.00002994919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001229313,"about_ca_topic_score_gemma":0.00002050661,"domain_scores_codex":[0.9986935,0.0000905061,0.0003061261,0.0004008776,0.0002154175,0.0002936257],"domain_scores_gemma":[0.9991378,0.00004656701,0.0001060718,0.0003825974,0.0002164555,0.0001105357],"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":[0.00001377733,0.0003399039,0.0006123958,0.00004175607,0.00002848073,0.00004919874,0.00101861,6.351725e-8,0.005830394,0.1252483,0.004593803,0.8622233],"study_design_scores_gemma":[0.0007892781,0.0004553662,0.005329548,0.0001906079,0.00002144062,0.000234991,0.0001847604,0.002665272,0.6387889,0.3473145,0.00307193,0.0009533186],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008742184,0.00002979868,0.8124244,0.0001684134,0.000163206,0.0002042355,0.000003950345,0.001520142,0.1767437],"genre_scores_gemma":[0.6217699,0.00004760061,0.3768007,0.0007635123,0.00007639863,0.00006925057,0.00002131149,0.00001498328,0.0004363942],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.86127,"threshold_uncertainty_score":0.9984552,"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."}}