{"id":"W2548004707","doi":"","title":"Improvement in handwritten numeral string recognition by slant normalization and contextual information","year":2004,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Numeral system; Normalization (sociology); NIST; String (physics); Pattern recognition (psychology); Artificial intelligence; Computer science; Speech recognition; Segmentation; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002583657,0.0001171302,0.0001140882,0.0002091413,0.00006797223,0.0002468957,0.0001559471,0.00007170507,0.00001929715],"category_scores_gemma":[0.00002829238,0.0001138079,0.00001961181,0.0002433382,0.00002096275,0.002964964,0.000094553,0.00009765066,0.00003608955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009991431,"about_ca_system_score_gemma":0.00003432371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004753445,"about_ca_topic_score_gemma":0.00009278636,"domain_scores_codex":[0.9990469,0.0000227981,0.0003563373,0.000182617,0.0001894959,0.0002019133],"domain_scores_gemma":[0.9996043,0.00001440376,0.00009364197,0.000126484,0.00009803893,0.00006305928],"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":[0.00001285572,0.00008324359,0.00052955,0.00003200521,0.000008242462,0.00000302536,0.001172163,0.00001034389,0.006235173,0.009677801,0.0005559325,0.9816797],"study_design_scores_gemma":[0.01134675,0.001588724,0.006412935,0.000523035,0.00001967251,0.0001263593,0.001098775,0.02397696,0.8658287,0.08248589,0.004903335,0.001688879],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1543004,0.00002266834,0.8432971,0.0005036815,0.00005287761,0.0003519809,0.00001087126,0.0002675541,0.001192846],"genre_scores_gemma":[0.9775495,0.00008407338,0.02118558,0.0009647889,0.00001364887,0.00006435607,0.0001094403,0.000004647747,0.0000239452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9799908,"threshold_uncertainty_score":0.4640953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008501452204767307,"score_gpt":0.215890411133911,"score_spread":0.2073889589291437,"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."}}