{"id":"W4377927260","doi":"10.2352/issn.2168-3204.2005.2.1.art00037","title":"Joint Compression and Restoration of Documents with Bleed-through","year":2005,"lang":"en","type":"article","venue":"Archiving Conference","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"University of Ottawa","keywords":"Computer science; Bleed; Compression (physics); Joint (building); Segmentation; Inpainting; Artificial intelligence; Computer vision; Image (mathematics); Engineering; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001292702,0.00009032362,0.0001283124,0.00006857439,0.00006996821,0.00008294941,0.0002525148,0.0000217153,0.00001417028],"category_scores_gemma":[0.00002062598,0.0000737153,0.00001393634,0.00009886266,0.00008752241,0.0007616013,0.0001850725,0.00009172766,0.000006829982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001213349,"about_ca_system_score_gemma":0.00004879069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005664426,"about_ca_topic_score_gemma":0.00002957695,"domain_scores_codex":[0.9992291,0.00006391975,0.000185246,0.0001988388,0.0001927471,0.0001301551],"domain_scores_gemma":[0.9994186,0.00005843831,0.0001208735,0.0002732751,0.00008482191,0.00004405988],"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":[0.00002609633,0.0001668502,0.00388065,0.00009771501,0.00003037849,0.00000602101,0.008864161,0.00002725292,0.1159418,0.1495287,0.0006902055,0.7207401],"study_design_scores_gemma":[0.001471281,0.001001312,0.07664423,0.002088168,0.00002661819,0.0001040335,0.0001497026,0.06180829,0.7698106,0.08054695,0.005544002,0.0008048652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1681679,0.00002500612,0.8245918,0.0007147567,0.00001727092,0.0001305186,0.000001291329,0.0001328042,0.006218697],"genre_scores_gemma":[0.7804348,0.00004465972,0.2193526,0.00007582755,0.00001232479,0.000009751488,0.000001786264,0.000003600157,0.00006462263],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7199353,"threshold_uncertainty_score":0.3006023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02762871088630275,"score_gpt":0.2634066191636564,"score_spread":0.2357779082773537,"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."}}