{"id":"W4409094360","doi":"10.1145/3727623","title":"<i>DeepHadad</i> : Enhancing Readability of Damaged Inscriptions with Synthetic Data","year":2025,"lang":"en","type":"article","venue":"Journal on Computing and Cultural Heritage","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Arab-German Young Academy of Sciences and Humanities","keywords":"Readability; Computer science; Archaeology; Geography; Programming language","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":[0.0007429552,0.0009860806,0.0003370397,0.000603107,0.0003079365,0.0009727533,0.001352971,0.0009151968,0.003012323],"category_scores_gemma":[0.00358033,0.0002770355,0.0006480628,0.0003768056,0.0006686313,0.001063181,0.001006131,0.00115217,0.001063632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005497107,"about_ca_system_score_gemma":0.000400323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006543049,"about_ca_topic_score_gemma":0.01323401,"domain_scores_codex":[0.9996995,0.00006770014,0.00001723385,0.0001153128,0.00006700969,0.00003326148],"domain_scores_gemma":[0.9990977,0.0003948786,0.00005410336,0.0002599851,0.0001460593,0.00004738027],"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":[0.0008177782,0.0003992559,0.006149264,0.0008727889,0.0002855795,0.0007457905,0.0007564709,0.3924605,0.05683127,0.003003421,0.02848855,0.5091892],"study_design_scores_gemma":[0.00004369637,0.000162469,0.001951827,0.00006206267,0.00003138723,0.0001418767,0.0001608789,0.944437,0.04201074,0.00266461,0.00829058,0.00004284843],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6268819,0.002178685,0.3149352,0.002050962,0.001528113,0.0003166365,0.009306252,0.02993247,0.01286985],"genre_scores_gemma":[0.7764468,0.0004126854,0.2010117,0.0005142076,0.00007798729,0.0001452742,0.0135728,0.001222316,0.006596228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006543049,"threshold_uncertainty_score":0.01300991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369113190217857,"score_gpt":0.2898824407683843,"score_spread":0.2661913088662057,"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."}}