{"id":"W4410924614","doi":"10.1007/978-3-031-93838-2_11","title":"Optical Character Recognition for Early Handwriting Legibility Assessment","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Writing and Handwriting Education","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Legibility; Handwriting; Computer science; Intelligent character recognition; Character (mathematics); Optical character recognition; Character recognition; Artificial intelligence; Speech recognition; Handwriting recognition; Sketch recognition; Document processing; Computer vision; Natural language processing; Pattern recognition (psychology); Feature extraction; Image (mathematics); Visual arts; Gesture recognition; Art","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":[0.0004795254,0.000758241,0.0004960936,0.001177124,0.0002663308,0.0009959025,0.0006715814,0.0008641998,0.009316502],"category_scores_gemma":[0.001635037,0.0003293018,0.0002783986,0.0006484152,0.0001980263,0.0008147893,0.0005143044,0.0004636713,0.004316959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002332733,"about_ca_system_score_gemma":0.0003449961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585864,"about_ca_topic_score_gemma":0.002863729,"domain_scores_codex":[0.999525,0.00007206217,0.00002951469,0.000118803,0.0002095392,0.00004508219],"domain_scores_gemma":[0.9983834,0.0005499745,0.0001252,0.0001335862,0.0007338495,0.00007395604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000354897,0.0001216677,0.002550314,0.0001999489,0.00001864046,0.00009751202,0.00006573517,0.001114531,0.19077,0.0002792396,0.002797947,0.8016296],"study_design_scores_gemma":[0.000067627,0.00161374,0.05697193,0.0003151458,0.0002384515,0.002136221,0.0003579468,0.2303407,0.6795887,0.001367508,0.02684629,0.0001558295],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2017178,0.005082096,0.7532378,0.0002472701,0.0004664033,0.0005218662,0.001162575,0.008390866,0.0291733],"genre_scores_gemma":[0.6044006,0.002479097,0.3394488,0.0002252484,0.0001363744,0.0003165295,0.001088836,0.0004650105,0.05143956],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009316502,"threshold_uncertainty_score":0.03116679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04056801210491031,"score_gpt":0.3481424996986611,"score_spread":0.3075744875937508,"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."}}