{"id":"W2767607030","doi":"10.3390/info8040142","title":"Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks","year":2017,"lang":"en","type":"article","venue":"Information","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Malaysia Pahang; Trent University; Nottingham Trent University","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Restricted Boltzmann machine; Digit recognition; Deep learning; Boltzmann machine; Feature extraction; Arabic numerals; Pattern recognition (psychology); Feature (linguistics); Speech recognition; Numerical digit; Artificial neural network; Handwriting recognition; Arithmetic; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002007779,0.0001514856,0.0001311969,0.0002329718,0.0005510625,0.001050428,0.0004357501,0.0001157932,0.00001877781],"category_scores_gemma":[0.0002194335,0.0001436968,0.00004561778,0.0001121741,0.00008164819,0.004715045,0.0001170685,0.0002065434,0.00007185007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004820269,"about_ca_system_score_gemma":0.00003214911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004704509,"about_ca_topic_score_gemma":0.000006355403,"domain_scores_codex":[0.9989483,0.00004332519,0.00032789,0.0001756942,0.000289616,0.0002151369],"domain_scores_gemma":[0.998791,0.00008654551,0.0003281145,0.0004797296,0.0002178724,0.00009670464],"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":[0.0000544476,0.00004053336,0.001393714,0.00002223992,0.000008813084,0.000004320031,0.00008797403,0.0003603106,0.00001949552,0.001117328,0.002940361,0.9939505],"study_design_scores_gemma":[0.0007855108,0.0001607608,0.04989723,0.00006090457,0.000005790623,0.00001965752,0.000003600876,0.943756,0.0005941497,0.001639336,0.002869107,0.0002079344],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03004895,0.00002212583,0.9581493,0.002760572,0.0003427498,0.0004816833,0.00005175636,0.0007088393,0.007434078],"genre_scores_gemma":[0.9925286,0.00002245024,0.00560842,0.001369613,0.00007115672,0.00005643587,0.0003016532,0.000005756951,0.0000359058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9937425,"threshold_uncertainty_score":0.9999866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01647702975498928,"score_gpt":0.2375908017093598,"score_spread":0.2211137719543705,"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."}}