Dysgraphia Detection Using Machine Learning-Based Techniques: A Survey

Author:

Agarwal Basant,Jain Sonal,Bansal Priyal,Shrivastava Sanatan,Mohan Navyug

Publisher

Springer Nature Singapore

Reference38 articles.

1. Solanki RB, Waghela SP, Shankarmani R (2020) Dysgraphia disease detection using handwriting analysis. Int J Adv Technol Eng Sci 08(04)

2. Dutt S, Ahuja NJ (2020) A novel approach of handwriting analysis for dysgraphia type diagnosis. Int J Adv Sci Technol 29(3):11812. http://sersc.org/journals/index.php/IJAST/article/view/29852

3. Drotar P, Dobeš M (2020) Dysgraphia detection through machine learning. Sci Rep 10(1):1–11. https://doi.org/10.1038/s41598-020-78611-9

4. Spoon K, Crandall D, Siek K (2019) Towards detecting dyslexia in children’s handwriting using neural Networks”; https://aiforsocialgood.github.io/icml2019/accepted/track1/pdfs/43_aisg_icml2019.pdf

5. Richard G, Serrurier M, Dyslexia and Dysgraphia prediction: a new machine learning approach. https://arxiv.org/abs/2005.06401

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