{"id":"W7098145901","doi":"","title":"Automatic Segmentation and Recognition System for Handwritten Dates on Canadian Bank Cheques","year":2002,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Cheque; Cursive; Set (abstract data type); Pattern recognition (psychology); Test set; Handwriting recognition","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003074035,0.0007514252,0.0007018456,0.002075047,0.001357087,0.0009949981,0.0008698236,0.0007285717,0.005766933],"category_scores_gemma":[0.0006747469,0.0003411994,0.0004504008,0.001045755,0.0003160537,0.0006449413,0.0003584588,0.0004400245,0.002488127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002011917,"about_ca_system_score_gemma":0.003605136,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1725485,"about_ca_topic_score_gemma":0.2690392,"domain_scores_codex":[0.9996408,0.00002103517,0.00001919224,0.0001168978,0.0001407134,0.00006136247],"domain_scores_gemma":[0.9993838,0.00004832205,0.00005647681,0.00004532702,0.0004017272,0.00006437705],"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":[0.0005490493,0.000119084,0.005685425,0.0003416578,0.00006487995,0.0004986699,0.0004586361,0.002662076,0.3546707,0.001185703,0.02366609,0.6100979],"study_design_scores_gemma":[0.0002108322,0.0005438569,0.1402667,0.0001634311,0.0003613019,0.00197444,0.00111806,0.1366696,0.528255,0.001004266,0.1890458,0.0003868194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4952677,0.003490646,0.389502,0.0005858677,0.000503278,0.001849104,0.01134251,0.06257691,0.03488207],"genre_scores_gemma":[0.4462486,0.001020558,0.499775,0.0003296152,0.0001523894,0.0005256346,0.01417783,0.0007728803,0.03699749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8274515,"threshold_uncertainty_score":0.3430883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03149555831714395,"score_gpt":0.2411098948328816,"score_spread":0.2096143365157377,"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."}}