{"id":"W2012442176","doi":"10.1016/j.patcog.2008.12.021","title":"Text line segmentation in handwritten documents using Mumford–Shah model","year":2009,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Morphing; Segmentation; Line (geometry); Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision; Image segmentation; Document processing; Document image processing; Natural language processing; Image (mathematics); Mathematics; Geometry","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":[0.0003698116,0.0002326706,0.0002236219,0.0004614234,0.0001030596,0.0002154383,0.0003732106,0.0001323735,0.00007344654],"category_scores_gemma":[0.00002734528,0.0002521663,0.00007996488,0.0004581769,0.00002511228,0.001593875,0.00007887456,0.0002163026,0.0001526308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001809487,"about_ca_system_score_gemma":0.00004212684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000814361,"about_ca_topic_score_gemma":0.00005281386,"domain_scores_codex":[0.9981236,0.0001066302,0.0005020637,0.0005241887,0.0003564118,0.0003871333],"domain_scores_gemma":[0.9992349,0.0000356456,0.0001851723,0.0003027929,0.0001429044,0.00009859069],"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":[0.000009345852,0.0001855975,0.0004154877,0.0000163026,0.000006555231,0.00001701856,0.0004087379,0.0001524795,0.0157463,0.00001672345,0.0001287322,0.9828967],"study_design_scores_gemma":[0.001523584,0.0002568122,0.002035141,0.0003724273,0.0000203404,0.00005264036,0.0000647593,0.8237476,0.1154984,0.05580203,0.00002499983,0.0006012741],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2640366,0.00002452639,0.7340443,0.0003052469,0.00009016591,0.000383129,0.0000153349,0.0002808039,0.0008198839],"genre_scores_gemma":[0.8904195,0.00006694702,0.1073007,0.001879633,0.00008173494,0.00005653138,0.000131141,0.00001649502,0.0000473121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9822955,"threshold_uncertainty_score":0.9999931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04366332411408295,"score_gpt":0.3050093866377097,"score_spread":0.2613460625236267,"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."}}