{"id":"W2015121333","doi":"10.5539/cis.v1n3p116","title":"Hough Transform and Its Application in Vehicle License Plate Tilt Correction","year":2008,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Hough transform; Tilt (camera); License; Computer vision; Artificial intelligence; Computer graphics (images); Image (mathematics); Geometry; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001525201,0.00006607367,0.00006533076,0.0002044199,0.0001184326,0.00005135462,0.00005346528,0.0000362342,0.000001560986],"category_scores_gemma":[0.000004898091,0.00006817515,0.00000691663,0.0004231481,0.00005377638,0.004468992,0.00001539101,0.0000810845,0.00003600767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003467649,"about_ca_system_score_gemma":0.00001305347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005818872,"about_ca_topic_score_gemma":0.000002753868,"domain_scores_codex":[0.9994799,0.000003814429,0.0001760548,0.00008225373,0.0001277317,0.0001302349],"domain_scores_gemma":[0.9997954,0.00001797047,0.00002241243,0.00005255219,0.00005524472,0.00005644975],"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":[0.00001296045,0.00001099996,0.0007117044,0.00006593124,0.000002595723,0.000001005499,0.007480853,0.0139846,0.002934715,0.0001620875,0.000114212,0.9745184],"study_design_scores_gemma":[0.0002577343,0.0000188158,0.04919504,0.00001451381,9.847603e-7,0.00007611272,0.00003009647,0.9417686,0.00726622,0.00001902224,0.001267232,0.00008556293],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9469706,0.00003331041,0.05038363,0.00003641078,0.0002019753,0.000170938,0.000001872924,0.0001126935,0.002088575],"genre_scores_gemma":[0.9990433,0.0004165916,0.0004038686,0.00009151262,0.0000230248,0.000009922166,0.000005725046,0.000002798412,0.000003213441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9744328,"threshold_uncertainty_score":0.3239911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009712091826851014,"score_gpt":0.1999615003179204,"score_spread":0.1902494084910694,"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."}}