{"id":"W4247646242","doi":"10.1145/3495018.3501199","title":"Handwritten character recognition based on CNN","year":2021,"lang":"en","type":"article","venue":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Character recognition; Computer science; Character (mathematics); Artificial intelligence; Speech recognition; Pattern recognition (psychology); Feature extraction; Intelligent word recognition; Natural language processing; Intelligent character recognition; Mathematics; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000300337,0.001032236,0.0007534788,0.001462158,0.0003049815,0.0009942485,0.0008986023,0.0006189912,0.01122708],"category_scores_gemma":[0.0008536307,0.0003516439,0.0007610992,0.001138897,0.0002118447,0.001349268,0.0005291667,0.0006224277,0.004552033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006759852,"about_ca_system_score_gemma":0.000716822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327252,"about_ca_topic_score_gemma":0.01362394,"domain_scores_codex":[0.9995574,0.0000286649,0.00002753416,0.0001314593,0.0001700667,0.0000849053],"domain_scores_gemma":[0.9996426,0.00005481968,0.00003174759,0.00006485854,0.0001772191,0.00002881497],"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":[0.0003274644,0.0001092375,0.001388805,0.0002373403,0.0001345825,0.0002565504,0.00002720885,0.01937905,0.06603561,0.0008417421,0.01622457,0.8950379],"study_design_scores_gemma":[0.00002958016,0.0002098605,0.005070616,0.0000711944,0.0001647319,0.0004230545,0.00004137943,0.8941694,0.08368589,0.001269475,0.01480803,0.0000568465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1934655,0.01193754,0.6967455,0.00118212,0.002770424,0.0005577336,0.003957293,0.02630602,0.06307792],"genre_scores_gemma":[0.6991083,0.005482059,0.2137713,0.0007123426,0.000630976,0.0002199926,0.008220392,0.0006171417,0.07123765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01327252,"threshold_uncertainty_score":0.03755832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06041791743990403,"score_gpt":0.3071205096829152,"score_spread":0.2467025922430112,"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."}}