Vision transformer-based model for early detection of dysgraphia among school students
Author:
Funder
Department of Science & Technology, Government of India
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00542-024-05741-9.pdf
Reference29 articles.
1. Agarwal B, Jain S, Bansal P, Shrivastava S, Mohan N (2023a) Dysgraphia detection using machine learning-based techniques: a survey. In International Conference On Emerging Trends In Expert Applications & Security. Springer, Singapore. pp 315–328
2. Agarwal B, Jain S, Beladiya K, Gupta Y, Yadav AS, Ahuja NJ (2023b) Early and automated diagnosis of dysgraphia using machine learning approach. SN Comput Sci 4(5):523
3. Asselborn T, Gargot T, Kidziński Ł, Johal W, Cohen D, Jolly C, Dillenbourg P (2018) Automated human-level diagnosis of dysgraphia using a consumer tablet. NPJ Digit Med 1(1):42
4. Asselborn T, Chapatte M, Dillenbourg P (2020) Extending the spectrum of dysgraphia: a data driven strategy to estimate handwriting quality. Sci Rep 10(1):3140
5. Beery KE (2004) Beery VMI: the Beery–Buktenica developmental test of visual-motor integration. Pearson, Minneapolis, MN
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