{"id":"W2099926249","doi":"10.1016/s0031-3203(00)00098-4","title":"A lexicon-driven approach for optimal segment combination in off-line recognition of unconstrained handwritten Korean words","year":2001,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Korea Science and Engineering Foundation","keywords":"Computer science; Lexicon; Artificial intelligence; Segmentation; Pattern recognition (psychology); Word (group theory); Classifier (UML); Intelligent word recognition; Handwriting recognition; Line (geometry); Speech recognition; Text segmentation; Image (mathematics); Character recognition; Feature extraction; Intelligent character recognition; 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":[0.0005541494,0.001447108,0.001844089,0.002074355,0.0008005163,0.001924478,0.002252594,0.001519621,0.006074976],"category_scores_gemma":[0.001612628,0.0008704923,0.001203175,0.002198177,0.0005185597,0.001614772,0.001453229,0.0009734242,0.004150603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007605245,"about_ca_system_score_gemma":0.001845313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008532769,"about_ca_topic_score_gemma":0.01900251,"domain_scores_codex":[0.9992477,0.0001249594,0.00007677748,0.0002009472,0.0002128096,0.0001367857],"domain_scores_gemma":[0.9990922,0.0003049502,0.00005943261,0.0000924851,0.0003993956,0.00005160046],"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":[0.000598404,0.0002198416,0.0008075878,0.0001833347,0.0000998621,0.0002671256,0.0001416855,0.03052662,0.07661059,0.001744719,0.006239803,0.8825603],"study_design_scores_gemma":[0.00007936882,0.0001664892,0.00112897,0.0000171199,0.0001192548,0.0002268573,0.0001810299,0.9464176,0.04429762,0.003323187,0.003995634,0.00004688529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04309423,0.0005790445,0.9452076,0.0001717506,0.0001136743,0.0002557735,0.0005685441,0.006962189,0.003047118],"genre_scores_gemma":[0.2879842,0.0004232966,0.6957229,0.0003774919,0.0001402199,0.0004514545,0.004225511,0.001258442,0.009416594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008532769,"threshold_uncertainty_score":0.0203228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04208097509528119,"score_gpt":0.2732928643360469,"score_spread":0.2312118892407657,"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."}}