{"id":"W4405965525","doi":"10.3390/s25010203","title":"Table Extraction with Table Data Using VGG-19 Deep Learning Model","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Table (database); Computer science; Row; Column (typography); Identification (biology); Data mining; Artificial intelligence; Machine learning; Task (project management); Database; Engineering; Frame (networking)","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":[0.0002018687,0.001497096,0.0007124236,0.001799603,0.0003083354,0.00144927,0.001745379,0.001065881,0.01004383],"category_scores_gemma":[0.0007242327,0.0004342411,0.001094575,0.002252832,0.0003020767,0.001742117,0.0008386328,0.0009617626,0.00714587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001186931,"about_ca_system_score_gemma":0.001191683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01703114,"about_ca_topic_score_gemma":0.03157905,"domain_scores_codex":[0.9997454,0.0000111741,0.00001823458,0.0001115588,0.00007071321,0.00004290548],"domain_scores_gemma":[0.9998355,0.00002543005,0.00001714001,0.0000522337,0.00005738161,0.00001227652],"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":[0.0004016514,0.000326196,0.003725302,0.0005516803,0.0001674026,0.0004278327,0.00008083703,0.06525517,0.01922963,0.003872272,0.1370788,0.7688833],"study_design_scores_gemma":[0.00006494606,0.0001929001,0.00351422,0.0001428881,0.00007093901,0.0003557096,0.0001568309,0.8921922,0.04243862,0.008422044,0.05238819,0.00006052189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1327495,0.005242683,0.5919439,0.001298508,0.001370195,0.001037001,0.1075327,0.1254923,0.0333332],"genre_scores_gemma":[0.3228249,0.001795425,0.4519505,0.0009191869,0.0001145897,0.0005538613,0.1900618,0.001432721,0.03034711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01703114,"threshold_uncertainty_score":0.03386402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04168891096115095,"score_gpt":0.3087875127823747,"score_spread":0.2670986018212237,"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."}}