{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002095486,0.00007423203,0.0001342966,0.0002150297,0.00002839283,0.0000107907,0.00004126753,0.00005782029,0.000005932748],"category_scores_gemma":[0.00001127089,0.00007792941,0.00003903952,0.0001110579,0.000017617,0.0003646658,0.000008414215,0.0001738931,0.000004125483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001627217,"about_ca_system_score_gemma":0.00002909717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001282492,"about_ca_topic_score_gemma":0.000002025168,"domain_scores_codex":[0.9994808,0.00004311169,0.0001509922,0.00005769075,0.0001803146,0.00008709424],"domain_scores_gemma":[0.9996226,0.00004590753,0.00008204757,0.00004246488,0.0001062803,0.0001006943],"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":[0.008173954,0.001168521,0.01320535,0.001060851,0.001491824,0.002110498,0.01780638,0.1627037,0.4174362,0.0002870405,0.002103746,0.3724519],"study_design_scores_gemma":[0.01362738,0.002018773,0.06017534,0.0009583798,0.0005858429,0.0005561734,0.005961387,0.7849698,0.128492,0.0003762221,0.001478795,0.0007998681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868683,0.000008330856,0.01247132,0.00006371368,0.0001879852,0.00006410584,0.000007841651,0.00002458168,0.0003038711],"genre_scores_gemma":[0.9957021,0.0000364306,0.004182412,0.00001429754,0.00004369271,4.943866e-8,0.00000389311,0.000009002188,0.000008088216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6222661,"threshold_uncertainty_score":0.3177869,"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."}}