{"id":"W4309355783","doi":"10.1002/9781119861850.ch10","title":"Smart Text Reader System for People who are Blind Using Machine and Deep Learning","year":2022,"lang":"en","type":"other","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Variety (cybernetics); Artificial intelligence; Sight; Computer science; Field (mathematics); Parsing; Deep learning; Data science; Population; Automation; Benchmark (surveying); Everyday life; Machine learning; Human–computer interaction; Engineering; Cartography; Geography; Medicine","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003361183,0.0002822862,0.0005278357,0.0004504802,0.0002319338,0.0002346657,0.0004860391,0.0002046595,0.001505838],"category_scores_gemma":[0.00003778928,0.0002676626,0.00009119107,0.0002907044,0.00002503898,0.00012909,0.0004464405,0.0003112297,0.00002095348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001011913,"about_ca_system_score_gemma":0.00003652842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004865909,"about_ca_topic_score_gemma":0.0005832173,"domain_scores_codex":[0.9984434,0.0001236462,0.0002473455,0.0006375783,0.0002580094,0.0002899829],"domain_scores_gemma":[0.9990276,0.0001036322,0.0003115365,0.0004099537,0.00005737732,0.00008990157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009896911,0.0003579425,0.01265389,0.01085432,0.0009318735,0.0001887571,0.004734345,0.00003908639,0.0004224653,0.02507834,0.4429733,0.5016667],"study_design_scores_gemma":[0.00135813,0.0002303883,0.0001039244,0.001253871,0.00009677358,0.0003084461,0.001197867,0.1379421,0.0004918965,0.0002384683,0.8554756,0.001302471],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00004206381,0.001481675,0.8367587,0.0001105875,0.0002264456,0.0009363943,0.0000286084,0.001919456,0.1584961],"genre_scores_gemma":[0.003085849,0.0002275727,0.3287947,0.0003637503,0.000345354,0.0004264346,0.0001060758,0.0006604161,0.6659899],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.507964,"threshold_uncertainty_score":0.9999775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208916007988205,"score_gpt":0.2607967330980513,"score_spread":0.2387075730181693,"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."}}