{"id":"W4386102201","doi":"10.1016/j.patrec.2023.08.008","title":"Handwriting word spotting in the space of difference between representations using vision transformers","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Spotting; Computer science; Artificial intelligence; Word (group theory); Natural language processing; Handwriting; Keyword spotting; Vocabulary; Speech recognition; Transformer; Pattern recognition (psychology); Binary number; Representation (politics); Arithmetic; Mathematics; Linguistics","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":[0.0003195282,0.0003587578,0.0004729044,0.0008110331,0.0001665654,0.001375646,0.0005086169,0.0003025577,0.003931506],"category_scores_gemma":[0.001805168,0.0001719536,0.0003547413,0.0008281537,0.0004963701,0.001767266,0.0007170092,0.0005972147,0.0007702958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003090971,"about_ca_system_score_gemma":0.0004371956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009696346,"about_ca_topic_score_gemma":0.0008119483,"domain_scores_codex":[0.9996992,0.00005545489,0.00002426851,0.00008349747,0.00009931167,0.00003830015],"domain_scores_gemma":[0.9993508,0.0002820453,0.00006753135,0.0001038831,0.0001389017,0.00005677536],"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":[0.001036844,0.0001064044,0.0009206065,0.000249201,0.00004389012,0.0002562891,0.0001870187,0.03418421,0.1649297,0.06741012,0.002920958,0.7277548],"study_design_scores_gemma":[0.00004071376,0.0002939605,0.001328708,0.00003237137,0.00002656257,0.0004499262,0.0001224605,0.8506432,0.09349978,0.0497666,0.003761072,0.00003465959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0605323,0.0001879799,0.9359758,0.00007462847,0.00005082913,0.00002733671,0.000104606,0.000995534,0.002050828],"genre_scores_gemma":[0.7496705,0.0002804573,0.2454125,0.00006281647,0.00004972396,0.00004160837,0.0002903641,0.0002924259,0.00389967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003931506,"threshold_uncertainty_score":0.01315218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05702812316307072,"score_gpt":0.316081109954259,"score_spread":0.2590529867911883,"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."}}