{"id":"W4311164911","doi":"10.18280/ts.390514","title":"ResNet and LSTM Based Accurate Approach for License Plate Detection and Recognition","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; Computer science; Artificial intelligence; Residual neural network; Feature extraction; Deep learning; Pattern recognition (psychology); Residual; Classifier (UML); Artificial neural network; Computer vision","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":[0.0002774704,0.001116441,0.0003983044,0.001191105,0.0002508099,0.0005280899,0.0009272803,0.0006580132,0.003239385],"category_scores_gemma":[0.000591083,0.0002688099,0.0007704499,0.0006772275,0.0002999813,0.001215384,0.0003911253,0.0008624335,0.002184001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005893615,"about_ca_system_score_gemma":0.0007288366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01082856,"about_ca_topic_score_gemma":0.01610199,"domain_scores_codex":[0.9997191,0.00003136959,0.00001761817,0.00007527349,0.0001038365,0.00005284254],"domain_scores_gemma":[0.9998234,0.00002496799,0.00002193567,0.00002154476,0.00009800997,0.00001017477],"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":[0.0004077387,0.0002823585,0.002468429,0.0003261721,0.0001947134,0.0006349082,0.0001106596,0.1052141,0.07405566,0.003710915,0.0110861,0.8015082],"study_design_scores_gemma":[0.00001246919,0.0001868516,0.003265669,0.0000296776,0.00007442046,0.0003475988,0.00008747958,0.9259552,0.06128353,0.001833617,0.006883339,0.00004019801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1228359,0.003414792,0.8422693,0.0008523166,0.0009257979,0.000211398,0.00135939,0.007838296,0.02029276],"genre_scores_gemma":[0.7734521,0.002123838,0.1840804,0.0004986041,0.0002699203,0.0001382831,0.003294898,0.0002227617,0.03591925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01082856,"threshold_uncertainty_score":0.02153105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02406627507227459,"score_gpt":0.2032221275970091,"score_spread":0.1791558525247345,"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."}}