[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85732-en":3,"doc-seo-85732-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85732,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Towards Objective Dysgraphia Detection: A Multi Branch Deep Learning Approach for Online Handwriting Analysis","Dysgraphia is a learning disability affecting school-age children, impairing handwriting coherence, quality, fluency, and legibility and thereby hindering academic progress. Diagnosis typically relies on subjective clinician observation, which is time-consuming and variable. A deep learning framework is proposed for objective dysgraphia detection from online handwriting captured by digitizing tablets, combining (i) handcrafted and embedding-based kinematic features from temporal signals and (ii) image-like representations derived via CWT and GAF, fused for improved performance. Results on the DiaGraMo dataset show the fusion of GAF, MOMENT, and kinematic features outperforms individual and alternative fusion schemes.","Towards Objective Dysgraphia Detection: A Multi-Branch Deep Learning Approach for Online  \nHandwriting Analysis  \nLydia Ouhib 1 , Yassine Ouzar1 , Zo Pinseel2 , Stphane Bouilland2 , and Mehdi Ammi 1  \n1 LIASD Laboratory, University of Paris 8, Saint-Denis, France  \n2 Centre Jacques Calv, Fondation Hopale, Berck, France  \narXiv :2607 .09826v1 [ cs .CV] 10 Jul 2026  \nAbstract—Dysgraphia is a specific learning disability that is prevalent among school-age children. It affects handwriting coherence, quality, fluency, and legibility, often hindering academic achievement and early learning development. This motor coordination disorder is typically diagnosed through subjective assessments based on clinician observation, which can be timeconsuming and prone to variability. In this paper, we introduce a deep learning-based framework for objective dysgraphia detection using online handwriting data captured via digitizing tablets. The proposed framework relies on two complementary branches: the first pipeline extracts both handcrafted and embedding-based kinematic features directly from raw temporal signals, while the second leverages image-based representations of the temporal signals generated using continuous wavelet transforms (CWT) and Gramian Angular Fields (GAF). The resulting features are then fused to leverage the complementary strengths of both representations. The four representations were evaluated separately and jointly using the publicly available DiaGraMo dataset, showing that the fusion of GAF, MOMENT, and hand-crafted kinematic features outperforms each individual representation, as well as other fusion schemes. These findings highlight the potential of the complementarity of image and signal based representations for more objective dysgraphia detection.  \nIndex Terms—Dysgraphia, handwriting analysis, kinematic features, GAF, CWT, MOMENT, multimodal fusion.  \nI. INTRODUCTION  \nMotor coordination refers to the set of processes through which the nervous system integrates signals from the brain, spinal cord, and peripheral nerves to control the timing, force, and precision of muscle activity, enabling smooth and accurate movements. This integration involves continuous feedback and feedforward communication between sensory systems and motor pathways, allowing even the simplest movements to be executed efficiently and adaptively [1] .  \nMotor coordination disorder affects about 5–6% of schoolaged children. Despite the absence of any apparent neurological lesion, it nevertheless leads to significant and persistent difficulties in acquiring motor skills [2] . The development of motor coordination in children is essential for performing daily activities such as writing, drawing, or manipulating objects. Despite the prevalence of this disorder and its long-term impact on quality of life, it remains underdiagnosed in many cases. Current clinical assessment relies on standardized motor  \n979-8-3195-1142-3/26/$31.00 ©2026 IEEE  \ntest batteries requiring qualified specialists, and is costly, timeconsuming, and inaccessible in many educational settings [2] .  \nMotor coordination disorders may also contribute to learning difficulties affecting writing, such as dysgraphia, which is a specific learning disorder characterized by slow, effortful, and illegible script [3], [4] . Handwriting analysis has emerged as a promising biomarker for early detection of motor and neurodevelopmental disorders [3] . Children with developmental coordination disorder and attention deficit hyperactive disorders show distinctive kinematic signatures in their handwriting : irregular velocity profiles, unpredictable acceleration peaks, excessive pen lifts, and unstable pressure patterns all of which can be objectively captured by digitizing tablets [3], [5] .  \nCurrent dysgraphia diagnosis is mainly based on clinical observation which is subjective, time-consuming, and varies between clinicians. Faced with these limitations, several studies have sought to auto","cbCaimWzCjD5iL4U","https://ap.wps.com/l/cbCaimWzCjD5iL4U","pdf",2257555,1,5,"English","en",105,"# Introduction\n# Materials and Methods\n## Dataset\n# Results and Discussion\n# Conclusion","[{\"question\":\"Why is dysgraphia diagnosis currently challenging in clinical practice?\",\"answer\":\"Diagnosis is mainly based on subjective clinician observation, which is time-consuming and can vary between clinicians, making it less objective.\"},{\"question\":\"What data and representations does the proposed method use?\",\"answer\":\"The method uses online handwriting signals captured by digitizing tablets and derives two complementary representations: kinematic features from raw temporal signals and image-based features from temporal signals using CWT and GAF.\"},{\"question\":\"How does the multi-branch fusion affect performance on the DiaGraMo dataset?\",\"answer\":\"Features from GAF, MOMENT, and handcrafted kinematic features are fused, and this fusion outperforms using any single representation or other tested fusion schemes.\"}]",1784205876,13,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"towards-objective-dysgraphia-detection-a-multi-branch-deep-learning-approach-for-online-handwriting-analysis","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/towards-objective-dysgraphia-detection-a-multi-branch-deep-learning-approach-for-online-handwriting-analysis/85732/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is dysgraphia diagnosis currently challenging in clinical practice?","Question",{"text":75,"@type":76},"Diagnosis is mainly based on subjective clinician observation, which is time-consuming and can vary between clinicians, making it less objective.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and representations does the proposed method use?",{"text":80,"@type":76},"The method uses online handwriting signals captured by digitizing tablets and derives two complementary representations: kinematic features from raw temporal signals and image-based features from temporal signals using CWT and GAF.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the multi-branch fusion affect performance on the DiaGraMo dataset?",{"text":84,"@type":76},"Features from GAF, MOMENT, and handcrafted kinematic features are fused, and this fusion outperforms using any single representation or other tested fusion schemes.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]