{"id":"W2171498274","doi":"10.1007/s10044-002-0169-3","title":"Large vocabulary off-line handwriting recognition: A survey","year":2003,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Vocabulary; Lexicon; Handwriting; Intelligent character recognition; Handwriting recognition; Key (lock); Natural language processing; Word recognition; Artificial intelligence; Software deployment; Speech recognition; Process (computing); Word (group theory); Line (geometry); Character recognition; Feature extraction; Linguistics; Reading (process); Software engineering","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":[0.0006148934,0.001060164,0.001686405,0.00331304,0.0003727482,0.00187456,0.001886633,0.0008934903,0.006078487],"category_scores_gemma":[0.002214279,0.0004112578,0.0006223017,0.005674174,0.0002421569,0.002520701,0.0006901924,0.0006090861,0.006930583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002582697,"about_ca_system_score_gemma":0.0009516253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002951528,"about_ca_topic_score_gemma":0.003240744,"domain_scores_codex":[0.9989694,0.0001046521,0.0001461957,0.0002560778,0.0004641004,0.00005955386],"domain_scores_gemma":[0.9981197,0.0006357358,0.0001417471,0.0002398361,0.0007840283,0.00007903384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006568003,0.0001141217,0.0008139834,0.0008389511,0.0000413064,0.00006205374,0.0000368466,0.001293078,0.006485716,0.0005845264,0.006015017,0.9836488],"study_design_scores_gemma":[0.0001462813,0.001669365,0.02228737,0.001266645,0.0006322897,0.007738322,0.001286546,0.3404433,0.1289299,0.01443173,0.4809033,0.0002649651],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.03875499,0.2200289,0.710629,0.0006526544,0.0008811377,0.0003852858,0.001585116,0.00632813,0.02075473],"genre_scores_gemma":[0.2063733,0.2881595,0.4138931,0.001540225,0.002365695,0.0006546167,0.01770385,0.001435009,0.06787467],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006078487,"threshold_uncertainty_score":0.02033454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02886706353236065,"score_gpt":0.2801902910180454,"score_spread":0.2513232274856847,"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."}}