{"id":"W1799095500","doi":"10.1007/978-3-642-04146-4_16","title":"Integration of Contextual Information in Online Handwriting Representation","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cursive; Artificial intelligence; Handwriting recognition; Robustness (evolution); Optimal distinctiveness theory; Pattern recognition (psychology); Intelligent character recognition; Handwriting; Classifier (UML); Representation (politics); Scripting language; Support vector machine; Machine learning; Character recognition; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002494396,0.0006601466,0.0007586512,0.0009280284,0.0002838955,0.000955431,0.0005789925,0.0004588088,0.004647545],"category_scores_gemma":[0.001054956,0.000349199,0.0004147575,0.001189309,0.0001878821,0.001386541,0.0008313337,0.0004760419,0.001708964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000159512,"about_ca_system_score_gemma":0.0004021889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001806235,"about_ca_topic_score_gemma":0.00386618,"domain_scores_codex":[0.9997216,0.00003648668,0.0000215535,0.00008472251,0.00009228662,0.00004340553],"domain_scores_gemma":[0.9995651,0.0001277439,0.00003983927,0.000108215,0.0001288709,0.00003019848],"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":[0.0003480466,0.0001064311,0.0004727204,0.0001513969,0.00002840114,0.00009077902,0.00004110551,0.0110278,0.09154383,0.001034709,0.002123349,0.8930314],"study_design_scores_gemma":[0.00003866441,0.0004226583,0.006026237,0.00008359148,0.0001817982,0.0004465648,0.0001186643,0.7947184,0.1802436,0.0046406,0.01301971,0.00005943304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0627358,0.00267491,0.9236107,0.0001208417,0.0002416392,0.00007828003,0.0004193494,0.005708914,0.004409572],"genre_scores_gemma":[0.5473805,0.002170831,0.4413476,0.0001156363,0.0002879549,0.0001195306,0.001329323,0.000676706,0.00657197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004647545,"threshold_uncertainty_score":0.01554763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02376910684700892,"score_gpt":0.2828602374181504,"score_spread":0.2590911305711415,"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."}}