{"id":"W2140602248","doi":"10.1109/icdar.2005.195","title":"Recognition for large sets of handwritten mathematical symbols","year":2005,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Handwriting recognition; Computer science; Artificial intelligence; Pattern recognition (psychology); Handwriting; Character recognition; Matching (statistics); Computation; Intelligent character recognition; Set (abstract data type); Intelligent word recognition; Feature extraction; Speech recognition; Algorithm; Mathematics; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009147448,0.0005747991,0.001029185,0.001275134,0.0005664153,0.001215182,0.001135706,0.0006735541,0.004210615],"category_scores_gemma":[0.005594911,0.0004095005,0.0006611011,0.00108912,0.0005238656,0.003053629,0.0011631,0.0009643648,0.002731547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003387144,"about_ca_system_score_gemma":0.0003256947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000406835,"about_ca_topic_score_gemma":0.0007149905,"domain_scores_codex":[0.9987159,0.0001741819,0.000131283,0.0003047037,0.0006048526,0.00006917281],"domain_scores_gemma":[0.9962109,0.00143062,0.000382639,0.001177432,0.0006943621,0.0001040646],"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":[0.0003159685,0.0001186334,0.002980473,0.0002823874,0.00008992839,0.000645101,0.000254902,0.02163836,0.1561073,0.009051196,0.004387437,0.8041282],"study_design_scores_gemma":[0.00004019924,0.0003164926,0.009090381,0.00008233448,0.0000809429,0.002546077,0.0003251085,0.6425648,0.2901866,0.02385344,0.03081885,0.00009484136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.133002,0.0004659313,0.8577857,0.0002793636,0.0001660618,0.0001140336,0.0003003056,0.003906061,0.003980509],"genre_scores_gemma":[0.4135023,0.0003342186,0.5800399,0.0001198207,0.00007151887,0.0001018236,0.0009157728,0.0002741397,0.00464053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004210615,"threshold_uncertainty_score":0.01408595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848239944110081,"score_gpt":0.2907152591452679,"score_spread":0.262232859704167,"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."}}