{"id":"W2096837246","doi":"10.1109/icfhr.2012.183","title":"Arabic handwritten word spotting using language models","year":2012,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Spotting; Computer science; Handwriting; Artificial intelligence; Natural language processing; Arabic; Classifier (UML); Word (group theory); Set (abstract data type); Handwriting recognition; Process (computing); Speech recognition; Identification (biology); Keyword spotting; Feature extraction; Linguistics; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0005060819,0.0001464759,0.0001569255,0.0001548559,0.0001202652,0.0001552689,0.0005051295,0.00007588503,0.00010191],"category_scores_gemma":[0.00002665304,0.0001299754,0.00007308293,0.0003486883,0.00002646592,0.002047513,0.0002553526,0.0001363592,0.0001214147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005092585,"about_ca_system_score_gemma":0.00002596724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009527951,"about_ca_topic_score_gemma":0.000003802866,"domain_scores_codex":[0.9987375,0.00006260349,0.0002224446,0.000247223,0.0002354537,0.0004948161],"domain_scores_gemma":[0.9992091,0.00005658969,0.0000710224,0.000439076,0.00006263124,0.0001615493],"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":[0.000003810713,0.0001830523,0.001619443,0.0000325962,0.00003032738,0.00002172798,0.004422384,0.00007529897,0.02560805,0.0412141,0.0007340395,0.9260552],"study_design_scores_gemma":[0.0006931126,0.0000592964,0.001112802,0.0001881108,0.00003215021,0.0003579421,0.0006322282,0.6266919,0.3408503,0.02619183,0.001873812,0.001316526],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1091254,0.0002896752,0.8779258,0.0001106338,0.0001300493,0.0001281776,8.419469e-7,0.0008818294,0.01140752],"genre_scores_gemma":[0.6539413,0.000005689791,0.345192,0.0004050373,0.0001200721,0.000008944216,0.000001197507,0.00001086945,0.0003148925],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9247386,"threshold_uncertainty_score":0.5300245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03757239094333532,"score_gpt":0.2838805264607805,"score_spread":0.2463081355174452,"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."}}