{"id":"W2353034237","doi":"","title":"Comparative Study on Template Matching and Neural Network Method to License Plate Character Recognition","year":2013,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Character (mathematics); Template matching; MATLAB; Artificial neural network; License; Character recognition; Artificial intelligence; Matching (statistics); Pattern recognition (psychology); Constructive; Component (thermodynamics); Speech recognition; Computer vision; Image (mathematics); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237699,0.0004323858,0.0004941998,0.001283089,0.0002527996,0.0006308952,0.0007124569,0.0005660977,0.002612137],"category_scores_gemma":[0.002701048,0.0001880624,0.0005378817,0.001321523,0.0002484581,0.001574357,0.0002961041,0.0003508865,0.0004038879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004268558,"about_ca_system_score_gemma":0.0004490132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004525967,"about_ca_topic_score_gemma":0.002903715,"domain_scores_codex":[0.9989052,0.0002671338,0.00006413828,0.0001766209,0.0005219493,0.00006492651],"domain_scores_gemma":[0.9991015,0.0003571706,0.00003884345,0.00006695851,0.0004078688,0.00002768146],"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":[0.0004506542,0.0001410066,0.004204876,0.0005069434,0.0001851203,0.0002689062,0.0001728428,0.04450957,0.02915261,0.006170249,0.002395579,0.9118416],"study_design_scores_gemma":[0.00003331174,0.0003602931,0.007400363,0.00004023669,0.0001525445,0.0006348768,0.0001480367,0.9342082,0.04709836,0.001575885,0.008286228,0.00006182786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1388011,0.01010381,0.83164,0.0002973964,0.0005372782,0.0001267709,0.00007988929,0.00125899,0.01715478],"genre_scores_gemma":[0.7787307,0.008583873,0.2003208,0.0001405336,0.0002857119,0.0001042801,0.0002636618,0.0001376158,0.01143279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004525967,"threshold_uncertainty_score":0.008999228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02665015363538115,"score_gpt":0.2731056871485471,"score_spread":0.246455533513166,"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."}}