{"id":"W2896024503","doi":"10.1109/access.2018.2876035","title":"Baidu Meizu Deep Learning Competition: Arithmetic Operation Recognition Using End-to-End Learning OCR Technologies","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Connectionism; Pipeline (software); Convolutional neural network; Task (project management); Artificial intelligence; Deep learning; End-to-end principle; Recurrent neural network; Pattern recognition (psychology); Artificial neural network; Machine learning","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":[0.004094606,0.002411368,0.0015236,0.001247973,0.001048152,0.001551995,0.002617188,0.002838748,0.01080438],"category_scores_gemma":[0.00262188,0.0005022328,0.0007219497,0.0009606773,0.0007273847,0.0025989,0.00240458,0.002311985,0.005538027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002063775,"about_ca_system_score_gemma":0.002955201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02122652,"about_ca_topic_score_gemma":0.04034408,"domain_scores_codex":[0.9980291,0.0002594795,0.00009993461,0.0004515998,0.000789824,0.0003700609],"domain_scores_gemma":[0.9984291,0.0001255384,0.00003374053,0.0002057383,0.0009604393,0.0002454595],"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":[0.001384102,0.0007341282,0.002271605,0.0003554772,0.0001631848,0.0003656601,0.0001147559,0.02066388,0.05601789,0.009505518,0.1324865,0.7759373],"study_design_scores_gemma":[0.0003636988,0.002097677,0.006754641,0.00007324224,0.0001269285,0.000601053,0.0001638618,0.6849715,0.1385356,0.006107151,0.1599566,0.0002480822],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1703238,0.003610458,0.6695837,0.003362398,0.004543157,0.003460917,0.009854508,0.02717086,0.1080902],"genre_scores_gemma":[0.4790671,0.0009679173,0.31569,0.00137532,0.0003701792,0.001176407,0.02288401,0.001091117,0.177378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02122652,"threshold_uncertainty_score":0.04220593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04740332064415651,"score_gpt":0.3155909041870854,"score_spread":0.2681875835429289,"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."}}