{"id":"W2139332812","doi":"10.1109/sibgra.1998.722790","title":"Morphological approach of handwritten word skew correction","year":2002,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Skew; Computer science; Word (group theory); Artificial intelligence; Heuristic; Task (project management); Speech recognition; Handwriting recognition; Convex hull; Pattern recognition (psychology); Regular polygon; Feature extraction; Mathematics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001961595,0.0001075172,0.0001791164,0.0001237169,0.00005268985,0.0000531319,0.0005049935,0.00009982666,0.0005913079],"category_scores_gemma":[0.00004393636,0.00008650671,0.00008046873,0.0004352077,0.00006499816,0.0002950992,0.0001377503,0.0001313349,0.0001392889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001915208,"about_ca_system_score_gemma":0.000005641337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000188191,"about_ca_topic_score_gemma":7.798708e-7,"domain_scores_codex":[0.9989735,0.0000653548,0.0002441021,0.0003118829,0.0002183963,0.0001867662],"domain_scores_gemma":[0.9993301,0.00005884475,0.00007828632,0.0003705083,0.00009569231,0.00006656154],"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":[0.000004888549,0.0006583876,0.0006186495,0.00002001069,0.000020397,0.00001955296,0.0003508985,0.00000345257,0.003747644,0.01650726,0.1027514,0.8752975],"study_design_scores_gemma":[0.001861332,0.00127709,0.008142012,0.0001223553,0.00003962523,0.001536315,0.0001944315,0.5689013,0.3549646,0.03600892,0.02537661,0.001575365],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003222331,0.00005703393,0.867611,0.0001832644,0.0001400351,0.0001429639,7.833067e-7,0.0005994947,0.1280431],"genre_scores_gemma":[0.7212492,0.00004691749,0.2745171,0.000245497,0.00003337044,0.00003277911,0.000002328943,0.000005739793,0.003867112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8737221,"threshold_uncertainty_score":0.6474407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03124099905645732,"score_gpt":0.2368856396100069,"score_spread":0.2056446405535496,"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."}}