{"id":"W157034376","doi":"10.1007/978-1-4471-4072-6_11","title":"Handwritten Farsi Word Recognition Using Hidden Markov Models","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hidden Markov model; Computer science; Artificial intelligence; Natural language processing; Word (group theory); Persian; Speech recognition; Arabic; Word recognition; Alphabet; Pattern recognition (psychology); Linguistics; Reading (process)","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005328869,0.0006996235,0.0006717165,0.0006840761,0.0002067693,0.0003967949,0.001239154,0.0008529799,0.002043342],"category_scores_gemma":[0.00001490854,0.0007053999,0.0003748152,0.0001202801,0.0001105724,0.001946033,0.0007118721,0.0005875116,0.0009391277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000261803,"about_ca_system_score_gemma":0.0001496943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004801181,"about_ca_topic_score_gemma":0.00001940807,"domain_scores_codex":[0.9968493,0.0000621212,0.0007369404,0.0009700977,0.0007078995,0.0006736441],"domain_scores_gemma":[0.9975654,0.0001128237,0.0004370852,0.001117889,0.0004469397,0.0003198965],"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":[0.000006476972,0.00002847631,8.487937e-7,0.00004199874,0.00006974478,0.0000232989,0.00008199394,3.098299e-7,0.00008615609,0.0313658,0.004877915,0.963417],"study_design_scores_gemma":[0.0004969647,0.000105222,0.000003487213,0.0010399,0.0001983397,0.0003482597,0.00001094358,0.01353155,0.003119772,0.9178769,0.0609672,0.002301453],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.00001563101,0.0005958463,0.4764995,0.0001385758,0.0003044911,0.0004762969,0.00003521856,0.001089524,0.5208449],"genre_scores_gemma":[0.001465606,0.001060599,0.699661,0.001198066,0.0007629657,0.00006290583,0.0001811475,0.0001875586,0.2954201],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9611155,"threshold_uncertainty_score":0.9998388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06494784647678918,"score_gpt":0.2579954598083946,"score_spread":0.1930476133316054,"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."}}