{"id":"W2970712536","doi":"10.48550/arxiv.1908.11550","title":"Handwritten Chinese Character Recognition by Convolutional Neural Network and Similarity Ranking","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Softmax function; Pattern recognition (psychology); Convolutional neural network; Artificial intelligence; Similarity (geometry); Computer science; Cross entropy; Ranking (information retrieval); Entropy (arrow of time); Euclidean distance; Artificial neural network; Mathematics; Image (mathematics)","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":[0.0005961416,0.0007054238,0.0008510748,0.001105658,0.0002651727,0.0008051689,0.0007140502,0.0005695449,0.001870185],"category_scores_gemma":[0.001092997,0.0002703691,0.0005124034,0.001477693,0.0003590466,0.001195749,0.0005107297,0.0005336877,0.0006873865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007793917,"about_ca_system_score_gemma":0.0006903796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009370632,"about_ca_topic_score_gemma":0.009258452,"domain_scores_codex":[0.9994335,0.00008230614,0.00003867715,0.0001453275,0.0002252425,0.00007483824],"domain_scores_gemma":[0.9996056,0.00008809103,0.00007301707,0.00007608412,0.0001288395,0.00002837687],"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":[0.0002730851,0.0001489929,0.002422004,0.0001423752,0.0001378664,0.0001801799,0.00004428212,0.1208696,0.04909142,0.002816601,0.003767004,0.8201067],"study_design_scores_gemma":[0.000006505222,0.0000624482,0.001829218,0.000007174748,0.00002151682,0.0001159882,0.00001121875,0.9765279,0.01979877,0.0006756522,0.0009284514,0.0000152029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2417591,0.003109526,0.7392216,0.0003198595,0.000267993,0.0001705798,0.0003348109,0.005377197,0.009439376],"genre_scores_gemma":[0.834421,0.000824369,0.1537319,0.0001069343,0.00009238804,0.00006626306,0.0006422359,0.0001014821,0.01001333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009370632,"threshold_uncertainty_score":0.01863217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04240639175468045,"score_gpt":0.1921913100682666,"score_spread":0.1497849183135861,"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."}}