{"id":"W2174657119","doi":"10.5815/ijitcs.2015.11.06","title":"Time and Accuracy Analysis of Skew Detection Methods for Document Images","year":2015,"lang":"en","type":"article","venue":"International Journal of Information Technology and Computer Science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Hough transform; Skew; Wavelet transform; Artificial intelligence; Radon transform; Pattern recognition (psychology); Robustness (evolution); Wavelet; Principal component analysis; Discrete wavelet transform; Computer vision; Image (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":[0.001871135,0.0005889685,0.0006698476,0.002599696,0.0004432683,0.0009443099,0.0007068265,0.0005087883,0.002057858],"category_scores_gemma":[0.0133338,0.0002598386,0.0005255666,0.001882565,0.0003661137,0.001390694,0.0004306638,0.000489035,0.0009528938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007550422,"about_ca_system_score_gemma":0.0004963431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001381169,"about_ca_topic_score_gemma":0.001080388,"domain_scores_codex":[0.9947759,0.0005586189,0.0003568847,0.0005360382,0.003571753,0.0002008885],"domain_scores_gemma":[0.9861031,0.006627419,0.001325755,0.001361927,0.004411749,0.0001700975],"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":[0.001361121,0.00009002919,0.006746859,0.0004758553,0.0001036246,0.0001584632,0.0002684316,0.01960112,0.1190112,0.001845795,0.001818013,0.8485196],"study_design_scores_gemma":[0.00007630138,0.001469136,0.03566343,0.0001021366,0.0002013996,0.002372728,0.0004556731,0.4546109,0.4894021,0.002405488,0.01307387,0.0001668839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3626295,0.007313464,0.6198074,0.000239969,0.0003521498,0.0001683661,0.0005288805,0.003502626,0.00545764],"genre_scores_gemma":[0.6728634,0.002162823,0.3193655,0.00004998893,0.0001208176,0.0001142484,0.001094436,0.0003829973,0.003845853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002599696,"threshold_uncertainty_score":0.009895623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132960431865673,"score_gpt":0.3309663503781305,"score_spread":0.3196367460594738,"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."}}