{"id":"W2789477730","doi":"10.1016/j.ins.2018.02.004","title":"An HMM framework based on spherical-linear features for online cursive handwriting recognition","year":2018,"lang":"en","type":"article","venue":"Information Sciences","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Hidden Markov model; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition; Handwriting recognition; Word recognition; Word (group theory); Segmentation; Feature extraction; Handwriting; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009166403,0.000152774,0.0001420217,0.0002936708,0.0006445262,0.0006523374,0.0008638513,0.0001225518,0.00006375474],"category_scores_gemma":[0.0007612127,0.0001299212,0.00006729146,0.0009306082,0.0003003869,0.004303298,0.00004687869,0.0001506672,0.00014625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004234389,"about_ca_system_score_gemma":0.0001362132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001878186,"about_ca_topic_score_gemma":0.000009891404,"domain_scores_codex":[0.998395,0.00006651555,0.0003859821,0.0002894789,0.0005443971,0.0003186325],"domain_scores_gemma":[0.9982157,0.0003882678,0.0002700181,0.0003125478,0.0006963278,0.0001171821],"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":[0.00003140399,0.0001140128,0.0000808669,0.00001902105,0.000003599372,4.325755e-7,0.001048316,0.0001498842,0.0002167089,0.005660589,0.002039409,0.9906358],"study_design_scores_gemma":[0.0005880623,0.003186616,0.001465511,0.0003716596,0.000008382113,0.00001293639,0.0006707662,0.8493425,0.07460734,0.05765603,0.01151492,0.0005753295],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009805114,0.000004636312,0.9845203,0.001388334,0.0003412787,0.0004007902,0.00005745381,0.0005012568,0.002980805],"genre_scores_gemma":[0.2961714,0.000003304876,0.6975526,0.005866271,0.0002665539,0.00006084914,0.00006680044,0.000004268456,0.000007945397],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9900604,"threshold_uncertainty_score":0.6290505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0405701509676921,"score_gpt":0.3424869457030411,"score_spread":0.3019167947353489,"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."}}