Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention
Handwriting difficulties can significantly affect education and daily functioning. Despite recent advances, challenges in standardization, dataset diversity, and model explainability still limit cross-study comparability and real-world applicability. This narrative review synthesizes recent work on...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11098793/ |
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| author | Suatmi Murnani Kazuharu Hashitsume Tomohiko Igasaki |
| author_facet | Suatmi Murnani Kazuharu Hashitsume Tomohiko Igasaki |
| author_sort | Suatmi Murnani |
| collection | DOAJ |
| description | Handwriting difficulties can significantly affect education and daily functioning. Despite recent advances, challenges in standardization, dataset diversity, and model explainability still limit cross-study comparability and real-world applicability. This narrative review synthesizes recent work on identifying and addressing handwriting difficulties, covering both traditional and technology-based approaches. Rather than proposing a new method or computational framework, it highlights trends, challenges, and opportunities in current research. We systematically reviewed 107 primary studies published between 2019 and 2024, sourced from IEEE Xplore and SpringerLink. Key techniques examined include the use of spatiotemporal signals, image data, feature-extraction methods, and algorithmic classification. We also explored key experimental design elements and assistive interventions aimed at improving handwriting performance. Our review underscores advances in handwriting analysis—particularly the role of technology in assessing and improving handwriting legibility and fluency—and shows how these methods address limitations of conventional assessments. The findings demonstrate the potential of technology-based approaches for assessing and mitigating handwriting difficulties, while revealing ongoing challenges such as limited dataset diversity, the need to integrate cognitive and motor aspects, and inadequate model transparency. Future work should focus on standardizing methods, increasing dataset representation, and developing interpretable, multimodal solutions. |
| format | Article |
| id | doaj-art-a3731c78846943dcb4a9f0fff62a99fc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a3731c78846943dcb4a9f0fff62a99fc2025-08-20T03:45:06ZengIEEEIEEE Access2169-35362025-01-011313339713341710.1109/ACCESS.2025.359324011098793Handwriting Difficulties: A Review of Recent Advances in the Identification and InterventionSuatmi Murnani0https://orcid.org/0000-0002-9050-651XKazuharu Hashitsume1https://orcid.org/0000-0002-4978-2651Tomohiko Igasaki2https://orcid.org/0000-0002-8745-5133Graduate School of Science and Technology, Kumamoto University, Kumamoto, JapanGraduate School of Education, Shimane University, Matsue, JapanFaculty of Advanced Science and Technology, Kumamoto University, Kumamoto, JapanHandwriting difficulties can significantly affect education and daily functioning. Despite recent advances, challenges in standardization, dataset diversity, and model explainability still limit cross-study comparability and real-world applicability. This narrative review synthesizes recent work on identifying and addressing handwriting difficulties, covering both traditional and technology-based approaches. Rather than proposing a new method or computational framework, it highlights trends, challenges, and opportunities in current research. We systematically reviewed 107 primary studies published between 2019 and 2024, sourced from IEEE Xplore and SpringerLink. Key techniques examined include the use of spatiotemporal signals, image data, feature-extraction methods, and algorithmic classification. We also explored key experimental design elements and assistive interventions aimed at improving handwriting performance. Our review underscores advances in handwriting analysis—particularly the role of technology in assessing and improving handwriting legibility and fluency—and shows how these methods address limitations of conventional assessments. The findings demonstrate the potential of technology-based approaches for assessing and mitigating handwriting difficulties, while revealing ongoing challenges such as limited dataset diversity, the need to integrate cognitive and motor aspects, and inadequate model transparency. Future work should focus on standardizing methods, increasing dataset representation, and developing interpretable, multimodal solutions.https://ieeexplore.ieee.org/document/11098793/Assistive technologyfeature extractionhandwriting analysishandwriting difficultyhandwriting rehabilitationmotor and cognitive assessment |
| spellingShingle | Suatmi Murnani Kazuharu Hashitsume Tomohiko Igasaki Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention IEEE Access Assistive technology feature extraction handwriting analysis handwriting difficulty handwriting rehabilitation motor and cognitive assessment |
| title | Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention |
| title_full | Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention |
| title_fullStr | Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention |
| title_full_unstemmed | Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention |
| title_short | Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention |
| title_sort | handwriting difficulties a review of recent advances in the identification and intervention |
| topic | Assistive technology feature extraction handwriting analysis handwriting difficulty handwriting rehabilitation motor and cognitive assessment |
| url | https://ieeexplore.ieee.org/document/11098793/ |
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