{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003853684,0.001091136,0.001252309,0.000849807,0.0003573304,0.000992634,0.001348587,0.001270868,0.01645159],"category_scores_gemma":[0.001429004,0.0002139547,0.0005733903,0.0002979116,0.0001911836,0.001605898,0.001168854,0.000869184,0.01611481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003010064,"about_ca_system_score_gemma":0.0004541901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001642684,"about_ca_topic_score_gemma":0.003422818,"domain_scores_codex":[0.9997247,0.00002776153,0.00003507279,0.00007620845,0.00008979077,0.00004660933],"domain_scores_gemma":[0.999602,0.0001124732,0.00003691842,0.00004880012,0.0001462468,0.00005355986],"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":[0.001594297,0.0004767729,0.004521175,0.001413582,0.0001656051,0.002394943,0.0002940556,0.001508969,0.03693246,0.0009600839,0.1641934,0.7855446],"study_design_scores_gemma":[0.00162592,0.004154261,0.04472549,0.001272371,0.001011929,0.0176712,0.00136758,0.3926749,0.2097135,0.008861654,0.3161781,0.0007430571],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2604937,0.0198822,0.3477007,0.002921947,0.003354895,0.003102898,0.02558956,0.2795587,0.05739543],"genre_scores_gemma":[0.5502416,0.006626137,0.2897924,0.003627996,0.00080681,0.001764515,0.03154421,0.002366955,0.1132294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01645159,"threshold_uncertainty_score":0.05503607,"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."}}