{"id":"W4400006653","doi":"10.18280/ts.410304","title":"A Comprehensive Literature Review of Vehicle License Plate Detection Methods","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; License; Task (project management); Artificial intelligence; Machine learning; Obstacle; Image (mathematics); Strengths and weaknesses; Face detection; Pattern recognition (psychology); Data mining; Facial recognition system; Engineering; Systems engineering; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002707085,0.0001871203,0.0002583474,0.000145205,0.00002916915,0.00004924763,0.00007341577,0.00008967005,0.0003030689],"category_scores_gemma":[0.000007663682,0.0001784458,0.0001346572,0.000497168,0.00001985976,0.000220728,0.00001561183,0.0002627287,0.00006864926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006075435,"about_ca_system_score_gemma":0.00001026088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002290811,"about_ca_topic_score_gemma":0.000001090863,"domain_scores_codex":[0.9989396,0.0001056607,0.0003757749,0.0001952567,0.0001855179,0.0001982182],"domain_scores_gemma":[0.999525,0.0001550981,0.00003402657,0.0001184907,0.0001022988,0.00006508588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002373166,0.0000246817,0.00000184236,0.02032263,0.0002006621,0.00005639489,0.0005240207,0.0007846447,0.5277171,0.00006061167,0.0007828482,0.4495008],"study_design_scores_gemma":[0.0008614363,0.0002418731,0.0006980595,0.04190548,0.0003799184,0.0002943564,0.00007383987,0.4617084,0.3805364,0.0003369795,0.1122624,0.0007008371],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.3376884,0.5382636,0.1150262,0.0003870515,0.001559862,0.001597513,0.0002111031,0.002032059,0.003234274],"genre_scores_gemma":[0.9312154,0.05917754,0.008619249,0.000359063,0.0003232858,0.00008758286,0.00009275253,0.00008693861,0.00003817462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.593527,"threshold_uncertainty_score":0.7276808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01626606207617557,"score_gpt":0.2789150399896906,"score_spread":0.262648977913515,"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."}}