{"id":"W2244599202","doi":"10.1007/978-3-642-38457-8_35","title":"Shape-Based Analysis for Automatic Segmentation of Arabic Handwritten Text","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scripting language; Segmentation; Spotting; Arabic; Artificial intelligence; Natural language processing; Text segmentation; Image segmentation; Market segmentation; Pattern recognition (psychology); Speech recognition; Linguistics","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":[0.0009365961,0.0004896149,0.0008573803,0.00247917,0.0001676156,0.0004318121,0.002430646,0.0003203699,0.0001808586],"category_scores_gemma":[0.000109889,0.0004480279,0.0004031714,0.001492967,0.000503255,0.0006749496,0.0003748014,0.0003310139,0.00003658215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000250359,"about_ca_system_score_gemma":0.0004444594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000275446,"about_ca_topic_score_gemma":0.00004081102,"domain_scores_codex":[0.9963608,0.00004789769,0.0008715227,0.00127669,0.0009303119,0.0005127458],"domain_scores_gemma":[0.9964472,0.0008529737,0.0006780569,0.001194541,0.0006853151,0.0001419352],"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":[0.000003146188,0.00004719455,0.00009556259,0.0001455492,0.00008351477,0.000004531412,0.0002257147,0.004820161,0.0008144559,0.002095866,0.00003692787,0.9916274],"study_design_scores_gemma":[0.0003313812,0.0002515131,0.0003119735,0.0003053842,0.00009773841,0.000005140932,1.755341e-7,0.9084201,0.02508033,0.06462839,0.00008141298,0.0004864355],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003995735,0.0001305316,0.9966834,0.0004236665,0.0002869713,0.001176959,0.00002010862,0.0002966987,0.0005820563],"genre_scores_gemma":[0.1666457,0.00001105146,0.8320923,0.0008219709,0.00009243949,0.0001255685,0.00003444454,0.00002903501,0.0001474832],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.991141,"threshold_uncertainty_score":0.9997972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01884433731927801,"score_gpt":0.2608355490324601,"score_spread":0.2419912117131821,"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."}}