{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001417058,0.0002477475,0.0002716011,0.0001307506,0.0001761975,0.0001768111,0.0001213197,0.0000717214,0.00003692571],"category_scores_gemma":[1.938574e-7,0.0002595553,0.00003702177,0.0002850593,0.00001130635,0.0002341058,0.0000648873,0.0002670138,0.001326122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004369436,"about_ca_system_score_gemma":0.000003354731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002789661,"about_ca_topic_score_gemma":0.0000096718,"domain_scores_codex":[0.9988182,0.00007831841,0.0003115456,0.000376669,0.0000964509,0.0003188121],"domain_scores_gemma":[0.9993495,0.0001722581,0.00005103938,0.0001958884,0.00007743494,0.0001539205],"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":[0.00004464152,0.0004369018,0.0005186095,0.00008323969,0.0003541687,0.000006639114,0.01054005,0.05032386,0.09213697,0.0000520813,0.005673375,0.8398294],"study_design_scores_gemma":[0.00644703,0.001270395,0.4218273,0.0005570079,0.0004867311,0.0005442977,0.002988342,0.4230156,0.03573234,0.01164908,0.09032838,0.005153482],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.750227,0.00001453616,0.2467762,0.0001375074,0.00003168572,0.002174502,0.00002486978,0.0003444609,0.0002692012],"genre_scores_gemma":[0.8157841,0.000004472303,0.1814003,0.0005431446,0.0003635803,0.001735433,0.00009584153,0.00005154006,0.00002157762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.834676,"threshold_uncertainty_score":0.9999857,"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."}}