{"id":"W2170654949","doi":"10.1109/icdar.2005.206","title":"Script identification using steerable Gabor filters","year":2005,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Gabor filter; Gabor wavelet; Artificial intelligence; Computer science; Computer vision; Gabor transform; Pattern recognition (psychology); Identification (biology); Filter (signal processing); Computation; Scripting language; Filter bank; Feature extraction; Rotation (mathematics); Time–frequency analysis; Algorithm","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":[0.000487684,0.0003374892,0.0005741092,0.001162538,0.0002592776,0.0007969429,0.0003201294,0.0005079652,0.001413806],"category_scores_gemma":[0.001344109,0.0002647207,0.000476438,0.0008011098,0.0003029344,0.001091521,0.0003268434,0.0003124311,0.001888371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002196751,"about_ca_system_score_gemma":0.0003133224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001274548,"about_ca_topic_score_gemma":0.001596702,"domain_scores_codex":[0.9995516,0.0001038048,0.00002360691,0.00008414579,0.0001855535,0.00005127772],"domain_scores_gemma":[0.9993598,0.0001941628,0.0001026567,0.0001528315,0.0001523315,0.00003817801],"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":[0.0003485047,0.00007503673,0.001888183,0.0001264166,0.00005396725,0.0003978273,0.0001235997,0.02202548,0.3532174,0.004019872,0.001743724,0.61598],"study_design_scores_gemma":[0.00003116723,0.0002593699,0.009928538,0.00003079561,0.0000685686,0.001417828,0.0001358359,0.6489891,0.3249322,0.003945005,0.01017113,0.00009035704],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07952161,0.0003258453,0.9151244,0.00006857757,0.0000483172,0.00003308092,0.00006583389,0.001816669,0.0029956],"genre_scores_gemma":[0.4909737,0.0005580274,0.5038131,0.00007951052,0.00005131485,0.00003126018,0.0002106444,0.0001179501,0.004164439],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001413806,"threshold_uncertainty_score":0.004729629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02858199877554003,"score_gpt":0.2767923243620172,"score_spread":0.2482103255864772,"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."}}