{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007654604,0.0004503826,0.0005987066,0.0004094111,0.0003872565,0.0007072665,0.0008110809,0.0002936775,0.0002511585],"category_scores_gemma":[0.0001025111,0.0004266785,0.0001500705,0.0009545526,0.0001004833,0.001467753,0.0002374412,0.0003588577,0.0002570142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430696,"about_ca_system_score_gemma":0.0001444089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001345307,"about_ca_topic_score_gemma":0.0007158059,"domain_scores_codex":[0.9961668,0.0001191849,0.0008455776,0.001148569,0.0007886131,0.0009312984],"domain_scores_gemma":[0.997259,0.0003210751,0.000349405,0.0007880401,0.001072106,0.0002103438],"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":[0.0001694271,0.0014706,0.000200466,0.0002938683,0.0002448407,0.00002147654,0.0003387152,0.000004267082,0.04630397,0.001247627,0.0104402,0.9392645],"study_design_scores_gemma":[0.005406099,0.001571876,0.001346761,0.001978869,0.0001881747,0.00003801335,0.0001360303,0.01095774,0.9414559,0.02377777,0.01067123,0.002471521],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06059623,0.00004629495,0.9009652,0.00773178,0.00007814387,0.00154252,0.0000814837,0.001761386,0.027197],"genre_scores_gemma":[0.2920867,0.000105257,0.6910732,0.01356869,0.0005833414,0.0006115611,0.0003805702,0.00009031793,0.001500372],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.936793,"threshold_uncertainty_score":0.9998185,"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."}}