{"id":"W1973691454","doi":"10.1109/icfhr.2012.169","title":"A Structure for Adaptive Handwriting Recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Handwriting recognition; Handwriting; Computer science; Pattern recognition (psychology); Artificial intelligence; Character (mathematics); Character recognition; Weight function; Class (philosophy); Training set; Intelligent character recognition; Test set; Set (abstract data type); Adaptation (eye); Speech recognition; Mathematics; Statistics; Feature extraction","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":[0.0002409582,0.000103781,0.0001048663,0.00009196231,0.000115647,0.00008595231,0.0002402619,0.00007511518,0.0001098273],"category_scores_gemma":[0.00006928214,0.00009245439,0.00006100206,0.0001667001,0.00001755279,0.001078399,0.00007195924,0.00007828345,0.00004904368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000029394,"about_ca_system_score_gemma":0.00001760194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006745851,"about_ca_topic_score_gemma":0.000004609668,"domain_scores_codex":[0.9991773,0.00003350796,0.0001543362,0.0001921799,0.0001190549,0.0003235804],"domain_scores_gemma":[0.9993777,0.0001241916,0.00006142728,0.0001785848,0.0001585198,0.00009962072],"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":[0.000008128025,0.00004803361,0.0001376539,0.00001598344,0.0000154402,4.675716e-7,0.0004113547,1.097321e-7,0.005123253,0.04194066,0.004108624,0.9481903],"study_design_scores_gemma":[0.0007207979,0.0002714337,0.0005891564,0.0000734242,0.00001858432,0.00008406841,0.0002125051,0.006887001,0.8165064,0.1664268,0.007616605,0.0005931915],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005297787,0.00005667593,0.9868357,0.000195134,0.000166407,0.0003602769,0.00002206199,0.000607816,0.006458171],"genre_scores_gemma":[0.5410139,0.000002340498,0.4582265,0.0004168775,0.0001580693,0.00006309864,0.00001083548,0.000006584843,0.0001018034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9475971,"threshold_uncertainty_score":0.3770181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03888323323101743,"score_gpt":0.271282896161667,"score_spread":0.2323996629306496,"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."}}