{"id":"W2148440013","doi":"10.1109/icfhr.2010.28","title":"Word Spotting in Gray Scale Handwritten Pashto Documents","year":2010,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Spotting; Artificial intelligence; Computer science; Handwriting; Pattern recognition (psychology); Word (group theory); Grayscale; Feature vector; Binary number; Handwriting recognition; Scripting language; Feature (linguistics); Speech recognition; Feature extraction; Natural language processing; Pixel; Mathematics; Arithmetic","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003936362,0.0007928228,0.001011808,0.001658426,0.0004189222,0.0008508693,0.000917334,0.0006219725,0.003252519],"category_scores_gemma":[0.001966965,0.0003210953,0.0005526361,0.001267636,0.0004578201,0.001080209,0.0006117632,0.0004788237,0.002047102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002495861,"about_ca_system_score_gemma":0.0003030855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009302257,"about_ca_topic_score_gemma":0.001146401,"domain_scores_codex":[0.9993979,0.00008061816,0.00005114866,0.0001634173,0.0002525448,0.00005443164],"domain_scores_gemma":[0.9987502,0.0004073016,0.0002355226,0.0002563607,0.0002740062,0.00007661159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000922274,0.00006290414,0.00135241,0.0004216466,0.00005244472,0.0008443715,0.000358234,0.003498264,0.2963722,0.0008301826,0.002071272,0.6932139],"study_design_scores_gemma":[0.00006841475,0.000660679,0.01411865,0.00007986512,0.0001389337,0.005822892,0.0006668976,0.1566945,0.802077,0.002941278,0.01662422,0.0001066994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3634317,0.001614448,0.6170201,0.0002295539,0.0002657441,0.0002502904,0.0003886773,0.01220884,0.004590687],"genre_scores_gemma":[0.4677878,0.0007228839,0.5221649,0.00011157,0.0000991318,0.00006619605,0.0005591333,0.0008000947,0.00768827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003252519,"threshold_uncertainty_score":0.01088071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007786585934421827,"score_gpt":0.2522058995031434,"score_spread":0.2444193135687215,"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."}}