{"id":"W4293868248","doi":"10.1109/crv55824.2022.00026","title":"Occluded Text Detection and Recognition in the Wild","year":2022,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Spotting; Computer science; Artificial intelligence; Text recognition; Text detection; Robustness (evolution); Convolutional neural network; Deep learning; Pattern recognition (psychology); Feature extraction; Speech recognition; Natural language processing; Computer vision; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006072159,0.001961011,0.00136932,0.001332919,0.0005191193,0.001309971,0.002196888,0.001262982,0.006254428],"category_scores_gemma":[0.002920598,0.0003876219,0.001034411,0.0009005415,0.0006220885,0.002992102,0.001597741,0.001233149,0.008940883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007031578,"about_ca_system_score_gemma":0.0006453869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006927141,"about_ca_topic_score_gemma":0.01146742,"domain_scores_codex":[0.9987912,0.00009990024,0.00005725245,0.0005452207,0.0003382638,0.0001682689],"domain_scores_gemma":[0.9986646,0.0002694831,0.0001264978,0.000539023,0.0002989213,0.0001014387],"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":[0.001907964,0.0005572107,0.005879106,0.0005829862,0.0002152504,0.001339577,0.0002200981,0.0354742,0.07564647,0.001352521,0.08110447,0.79572],"study_design_scores_gemma":[0.0001426609,0.0006094842,0.01100928,0.00007503009,0.0001254519,0.0009264056,0.0003665174,0.8440827,0.1096618,0.003452535,0.02946985,0.00007818199],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.588065,0.004174167,0.2375462,0.0009335144,0.001379486,0.0005813144,0.0210529,0.1231311,0.02313632],"genre_scores_gemma":[0.7768996,0.0008732966,0.1281891,0.0006611963,0.0003155708,0.0002701648,0.06430645,0.001524776,0.02695989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006927141,"threshold_uncertainty_score":0.0209232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02048468726008458,"score_gpt":0.2385355945911143,"score_spread":0.2180509073310298,"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."}}