{"id":"W4200466983","doi":"10.1109/iscc53001.2021.9631255","title":"Hand Gesture Recognition Using CNN &amp; Publication of World's Largest ASL Database","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Symposium on Computers and Communications (ISCC)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Sign language; Gesture; RGB color model; American Sign Language; Gesture recognition; Artificial intelligence; Alphabet; Sign (mathematics); Layer (electronics); Natural language processing; Speech recognition; Pattern recognition (psychology); Database","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"],"consensus_categories":[],"category_scores_codex":[0.0005871677,0.0002576934,0.0003728079,0.0004048167,0.0005351397,0.0006007518,0.001189494,0.0001196707,0.00003512718],"category_scores_gemma":[0.00005763357,0.0002657712,0.0001194858,0.001421833,0.0001667598,0.0006864708,0.0006777033,0.0003631504,0.00008165606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006678647,"about_ca_system_score_gemma":0.0001846224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007634302,"about_ca_topic_score_gemma":0.0002479144,"domain_scores_codex":[0.997479,0.0006327657,0.0006254318,0.0006096365,0.0003717482,0.0002813793],"domain_scores_gemma":[0.9951656,0.0004847198,0.0003865295,0.002889168,0.0008964424,0.0001774721],"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":[0.0001214937,0.006959656,0.00538704,0.0009704281,0.00128579,0.00008415958,0.01105408,0.001858896,0.2779124,0.0719498,0.05807433,0.564342],"study_design_scores_gemma":[0.006977961,0.0005318073,0.004225438,0.00483617,0.0004931181,0.001128181,0.0006428109,0.3668747,0.07167935,0.004186763,0.5346363,0.003787488],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0337285,0.002614237,0.9215593,0.02387292,0.002105589,0.0009275253,0.0003009795,0.0002275891,0.01466338],"genre_scores_gemma":[0.7286604,0.001662289,0.2650276,0.001730588,0.0003721502,0.0001130479,0.001409835,0.00005074649,0.0009734132],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6949319,"threshold_uncertainty_score":0.9999794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06683967639533489,"score_gpt":0.2926921146411678,"score_spread":0.2258524382458329,"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."}}