{"id":"W2363513225","doi":"","title":"Segmentation algorithm of license plate characters based on template matching and vertical projection","year":2015,"lang":"en","type":"article","venue":"Journal of Qiqihar University","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"License; Segmentation; Artificial intelligence; Character (mathematics); Adaptability; Computer science; Computer vision; Projection (relational algebra); Key (lock); Matching (statistics); Template matching; Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics","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":[0.0004245002,0.0008824517,0.0008836451,0.002249838,0.0005855458,0.001307163,0.001429762,0.0009427238,0.003802917],"category_scores_gemma":[0.001038244,0.0005308528,0.0009993293,0.002129781,0.0005800793,0.001852227,0.0006478649,0.0008331859,0.002341727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004922328,"about_ca_system_score_gemma":0.001202489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004430748,"about_ca_topic_score_gemma":0.002841523,"domain_scores_codex":[0.9990863,0.00006103412,0.00006508112,0.0002801739,0.0004098585,0.00009766471],"domain_scores_gemma":[0.9995516,0.0000717035,0.00004164057,0.00006810981,0.0002439046,0.00002308215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001773687,0.00006319514,0.001793955,0.0002316989,0.00007578154,0.0002662148,0.0002068645,0.011829,0.1514257,0.004411757,0.003259972,0.8262585],"study_design_scores_gemma":[0.00005662198,0.0002918245,0.008843234,0.00005388668,0.0001812415,0.002658595,0.0002661276,0.5123993,0.4468304,0.004010504,0.02425121,0.0001569616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01290205,0.0002415767,0.9825988,0.00004799593,0.00007779113,0.00009129873,0.00007616264,0.001785735,0.002178627],"genre_scores_gemma":[0.1754579,0.0007407381,0.814146,0.00009376823,0.00007725355,0.0001741389,0.0007623394,0.0005625141,0.007985337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004430748,"threshold_uncertainty_score":0.01272207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751283617312028,"score_gpt":0.2051420220004133,"score_spread":0.187629185827293,"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."}}