{"id":"W2591127855","doi":"","title":"“One Against One” or “One Against All”: Which One is Better for Handwriting Recognition with SVMs?","year":2006,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":205,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Handwriting; Support vector machine; Computer science; Handwriting recognition; Pattern recognition (psychology); Artificial intelligence; Character (mathematics); Point (geometry); Speech recognition; Class (philosophy); Intelligent character recognition; Character recognition; Machine learning; Feature extraction; Mathematics; Image (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":[0.005883843,0.002713515,0.003032293,0.001675265,0.0006155311,0.002442354,0.001719771,0.002746766,0.003936465],"category_scores_gemma":[0.01891215,0.0005854643,0.001449939,0.001449227,0.001114151,0.005453491,0.0009863422,0.00250584,0.002782125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000384723,"about_ca_system_score_gemma":0.0005809119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007079628,"about_ca_topic_score_gemma":0.001404975,"domain_scores_codex":[0.995874,0.001981365,0.0004573071,0.0007012141,0.0006991032,0.0002870231],"domain_scores_gemma":[0.9894577,0.006449631,0.0009475956,0.00121434,0.001577428,0.0003532486],"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":[0.001598063,0.0003374011,0.005081515,0.001258656,0.0008246577,0.0001884811,0.0001214711,0.03307822,0.0135332,0.005596042,0.007829833,0.9305525],"study_design_scores_gemma":[0.0003947725,0.003104595,0.01454433,0.0005652849,0.001044526,0.002445705,0.0004515194,0.8601391,0.05965459,0.04608399,0.01114476,0.000426898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1233627,0.01308021,0.8484916,0.003902866,0.0008423859,0.000253786,0.0005076428,0.004584853,0.004973959],"genre_scores_gemma":[0.5669144,0.003795043,0.4236761,0.0009212804,0.0005217763,0.0001494906,0.0006670532,0.0008431229,0.002511689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005883843,"threshold_uncertainty_score":0.03111708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04277814179642096,"score_gpt":0.2567714621377233,"score_spread":0.2139933203413024,"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."}}