{"id":"W3092214631","doi":"10.1007/978-3-030-59830-3_33","title":"License Plate Detection and Recognition by Convolutional Neural Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"License; Computer science; Convolutional neural network; Artificial intelligence; Process (computing); Optical character recognition; Deep learning; Truck; Character recognition; Pattern recognition (psychology); Computer vision; Image (mathematics); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002137838,0.0009085693,0.0004515116,0.0008860927,0.0001722052,0.0009367258,0.001163846,0.0006593459,0.007053202],"category_scores_gemma":[0.0006289926,0.0005423742,0.0005531784,0.0008525126,0.0002715013,0.0008890662,0.0006456342,0.0007275018,0.004943255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005271274,"about_ca_system_score_gemma":0.0004715255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009119207,"about_ca_topic_score_gemma":0.01537791,"domain_scores_codex":[0.999795,0.00001499115,0.000008349381,0.00005287444,0.00008588329,0.00004282025],"domain_scores_gemma":[0.9997836,0.00006694226,0.00002331594,0.00003885488,0.00007668282,0.00001064795],"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":[0.0001677495,0.0000906044,0.0010216,0.0001670139,0.00008193072,0.00009849281,0.00002333587,0.05799039,0.06013859,0.003455434,0.0154794,0.8612855],"study_design_scores_gemma":[0.000007011107,0.00004098587,0.002337877,0.00003246701,0.00004639327,0.0001567396,0.00001754411,0.9253123,0.05584791,0.003027109,0.01314869,0.00002496384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0395017,0.005252546,0.9246021,0.0003477352,0.0004600119,0.00009040513,0.001047043,0.006679662,0.02201887],"genre_scores_gemma":[0.4598,0.005614005,0.4137071,0.0003194397,0.0003353991,0.0001171046,0.004773768,0.00051809,0.1148151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009119207,"threshold_uncertainty_score":0.02359527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231177943865852,"score_gpt":0.1901794075972637,"score_spread":0.1778676281586052,"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."}}