{"id":"W2494353616","doi":"10.4018/978-1-4666-3970-6.ch005","title":"Selection of an Optimal Set of Features for Bengali Character Recognition","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Bengali; Artificial intelligence; Computer science; Classifier (UML); Character encoding; Alphabet; Character (mathematics); Pattern recognition (psychology); Feature (linguistics); Set (abstract data type); Natural language processing; Feature selection; Consonant; Mathematics; Speech recognition; Vowel; Linguistics","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":[0.000354994,0.0009999755,0.000985051,0.002080912,0.0004152696,0.001192624,0.0009074001,0.0005927326,0.005171405],"category_scores_gemma":[0.001210354,0.0002627566,0.000761377,0.001917568,0.0002843837,0.001167738,0.0004460376,0.0005986848,0.003026641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004863203,"about_ca_system_score_gemma":0.0004966562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001867909,"about_ca_topic_score_gemma":0.001644787,"domain_scores_codex":[0.999626,0.00004312017,0.00003481178,0.0001098568,0.0001209921,0.0000652589],"domain_scores_gemma":[0.9997175,0.00008748267,0.00003008301,0.00003594177,0.0001144197,0.00001458271],"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":[0.0001448892,0.00007317043,0.001192586,0.0003336651,0.00004229113,0.0002561353,0.0001123582,0.006688377,0.05748906,0.003004648,0.008238794,0.9224241],"study_design_scores_gemma":[0.00009755716,0.0006537762,0.02949728,0.0005096215,0.0005260584,0.003151403,0.001181234,0.5442566,0.2527163,0.0253966,0.141775,0.0002386047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07577805,0.006954624,0.8890288,0.0006055547,0.0002942962,0.0003626464,0.001480514,0.004694396,0.02080116],"genre_scores_gemma":[0.4086539,0.002751415,0.5696623,0.000152215,0.0001601901,0.0004271973,0.003781438,0.0004970092,0.01391432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005171405,"threshold_uncertainty_score":0.01730013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670371563845562,"score_gpt":0.2670353191727851,"score_spread":0.2403316035343295,"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."}}