{"id":"W2053642301","doi":"10.1016/j.patcog.2014.09.025","title":"Tensor representation learning based image patch analysis for text identification and recognition","year":2014,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fundamental Research Funds for the Central Universities; Guangdong Academy of Sciences; National Natural Science Foundation of China","keywords":"Artificial intelligence; Pattern recognition (psychology); Subspace topology; Computer science; Robustness (evolution); Tensor (intrinsic definition); Arabic numerals; Representation (politics); Classifier (UML); Identification (biology); 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":[0.0004706523,0.0007555553,0.0009675974,0.00151154,0.0004413358,0.0008285942,0.0007939643,0.0005837504,0.00368032],"category_scores_gemma":[0.001502416,0.0002620052,0.0008386252,0.002104029,0.0004799419,0.001461764,0.0006232724,0.0009530589,0.001786188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005791155,"about_ca_system_score_gemma":0.000822645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00766322,"about_ca_topic_score_gemma":0.007807015,"domain_scores_codex":[0.9996026,0.00007395531,0.00002749759,0.0001087265,0.0001323956,0.00005483175],"domain_scores_gemma":[0.9989563,0.0001938277,0.0001392323,0.0002383921,0.0004162294,0.00005607543],"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":[0.0002379004,0.0001268138,0.001256926,0.0002205574,0.00007609483,0.0000797096,0.0001024984,0.02732673,0.09577834,0.008394607,0.009865964,0.8565338],"study_design_scores_gemma":[0.000007409377,0.00009674757,0.001640392,0.00001414318,0.0000505381,0.0001061472,0.00006417552,0.9543096,0.03225182,0.006449322,0.00498073,0.00002887714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01718379,0.0005331316,0.9786889,0.0001701815,0.00008788618,0.00005922408,0.0003654978,0.002119402,0.0007919282],"genre_scores_gemma":[0.2913343,0.001365519,0.6976304,0.0001382764,0.00015541,0.0001346315,0.001516242,0.0004462456,0.007278965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00766322,"threshold_uncertainty_score":0.01523721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03298347598447665,"score_gpt":0.285156337496237,"score_spread":0.2521728615117604,"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."}}