{"id":"W4412081561","doi":"10.1109/tcsvt.2025.3586805","title":"Style-Preserving Generator for Synthetic License Plate Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science and Technology Council","keywords":"Computer science; License; Generator (circuit theory); Style (visual arts); Artificial intelligence; Speech recognition; Computer vision; Pattern recognition (psychology); Power (physics)","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":[0.0007901731,0.001428253,0.0005692542,0.000908972,0.000264024,0.0009462035,0.002015528,0.0008002803,0.004468287],"category_scores_gemma":[0.003145641,0.0003852173,0.0009670302,0.0008202849,0.000705574,0.001127217,0.001048016,0.001499699,0.00389478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006951867,"about_ca_system_score_gemma":0.0006194173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003995362,"about_ca_topic_score_gemma":0.005056629,"domain_scores_codex":[0.9991955,0.0001703128,0.00004695907,0.0002850333,0.0002275495,0.00007476166],"domain_scores_gemma":[0.9987283,0.0003628446,0.00009061908,0.0004886465,0.0002657833,0.00006378548],"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":[0.0006624726,0.0004814688,0.009407991,0.0005751233,0.0002149427,0.0006825233,0.0002257921,0.345004,0.04682265,0.006201614,0.02961843,0.560103],"study_design_scores_gemma":[0.00003713933,0.0001623425,0.002469593,0.00002543648,0.0000260097,0.0004119454,0.00008736225,0.928044,0.0559194,0.002799973,0.009958422,0.00005837835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2201752,0.001208603,0.7227262,0.0005566941,0.0007524367,0.0005997192,0.01299349,0.02639029,0.01459732],"genre_scores_gemma":[0.693271,0.0004019979,0.2573996,0.0004410804,0.00009261978,0.000457784,0.03529524,0.0009383832,0.0117023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004468287,"threshold_uncertainty_score":0.01494795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183316803923871,"score_gpt":0.233628531936184,"score_spread":0.2117953638969453,"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."}}