{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002477556,0.0001371435,0.0001428679,0.0002837908,0.0001923332,0.0003017602,0.0002058385,0.00007769171,0.0001490708],"category_scores_gemma":[0.00003922118,0.0001262511,0.00003548902,0.0001684505,0.00002295993,0.0005689663,0.00002535058,0.00005660253,0.0001505145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001498906,"about_ca_system_score_gemma":0.00002242138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003088019,"about_ca_topic_score_gemma":0.003079067,"domain_scores_codex":[0.9990028,0.00005194336,0.000225427,0.0003280337,0.0001376612,0.0002541643],"domain_scores_gemma":[0.9993129,0.0001211053,0.00006723454,0.0002176414,0.0001113422,0.0001697621],"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":[0.00000135051,0.00002625946,0.00004608401,0.0001221579,0.00001685376,0.000004966296,0.0003788881,1.739871e-7,0.0007085242,0.002775833,0.007301698,0.9886172],"study_design_scores_gemma":[0.001915422,0.001115752,0.0008832134,0.0008612835,0.00006291688,0.0001839014,0.0006518369,0.5035782,0.470078,0.01600698,0.003482794,0.00117969],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1770971,0.0003271716,0.7482888,0.006902257,0.0006995194,0.004516975,0.000202692,0.005580254,0.05638523],"genre_scores_gemma":[0.8610887,0.00003708468,0.1370702,0.0009567106,0.00005846387,0.0003502693,0.00005467448,0.00001625691,0.0003676714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9874375,"threshold_uncertainty_score":0.514837,"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."}}