{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008176079,0.00100127,0.001045943,0.0007637289,0.0003402543,0.001159964,0.001011166,0.0008725005,0.003317013],"category_scores_gemma":[0.004627258,0.0004886904,0.0006772708,0.00132242,0.0004846259,0.002160229,0.0008420676,0.0008044599,0.003488128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003736408,"about_ca_system_score_gemma":0.0006325066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003058426,"about_ca_topic_score_gemma":0.004352392,"domain_scores_codex":[0.9991742,0.0001621829,0.00004412211,0.0002193497,0.000319476,0.00008070502],"domain_scores_gemma":[0.9986302,0.0004667433,0.0001503412,0.0003717277,0.0003253923,0.00005570088],"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":[0.0004113365,0.0001607473,0.002490127,0.0001821551,0.00005368593,0.0002135619,0.0001196348,0.04694176,0.1614278,0.0017189,0.002312451,0.7839678],"study_design_scores_gemma":[0.00002161041,0.0003451202,0.009872886,0.00004044666,0.00007866765,0.0005948428,0.0001043122,0.8183436,0.1604116,0.002466858,0.00764181,0.00007825864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09864498,0.001406422,0.8775345,0.0002026695,0.0001683648,0.00005844262,0.0001722236,0.01602549,0.005786885],"genre_scores_gemma":[0.523985,0.00083582,0.4658912,0.0001827752,0.0001321601,0.00006500055,0.0008054605,0.0008232272,0.007279374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003317013,"threshold_uncertainty_score":0.01109648,"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."}}