{"id":"W19096729","doi":"10.1007/978-3-319-11656-3_21","title":"End-Shape Recognition for Arabic Handwritten Text Segmentation","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"King Abdulaziz University","keywords":"Computer science; Spotting; Arabic; Text segmentation; Segmentation; Artificial intelligence; Natural language processing; Handwriting; Text recognition; Handwriting recognition; Speech recognition; Word (group theory); Pattern recognition (psychology); Feature extraction; Linguistics; 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":[0.001469514,0.0006338834,0.000628731,0.001259586,0.0003659405,0.0008056617,0.002353476,0.0004823026,0.0001187632],"category_scores_gemma":[0.000186428,0.0006232968,0.0002373236,0.0005197115,0.0004989972,0.000847108,0.0005884409,0.0006154993,0.000175194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003043476,"about_ca_system_score_gemma":0.0003649243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001234588,"about_ca_topic_score_gemma":0.00004700745,"domain_scores_codex":[0.9956459,0.00006269021,0.0007606448,0.001844357,0.0009408209,0.0007455547],"domain_scores_gemma":[0.9966893,0.000880736,0.0004994045,0.001073679,0.0006558856,0.0002010036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007512229,0.00002027109,0.000007362285,0.00006451846,0.000009996338,0.000008435571,0.0001907225,0.00009897629,0.0006717509,0.00297527,0.0001179502,0.9958273],"study_design_scores_gemma":[0.0008290451,0.0006607564,0.00005344601,0.0009008293,0.00003010994,0.0001184175,3.087996e-7,0.3937348,0.04490785,0.5531445,0.004252361,0.001367621],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007483533,0.0001486321,0.9934617,0.0008411076,0.001164444,0.001315984,0.00003083964,0.0005493835,0.002413125],"genre_scores_gemma":[0.03120427,0.00008789342,0.9636022,0.003326633,0.0008460836,0.0002091857,0.0001228737,0.00007459924,0.0005262126],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9944596,"threshold_uncertainty_score":0.9996218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02355854329716926,"score_gpt":0.2611351952739788,"score_spread":0.2375766519768095,"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."}}