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Early and Automated Diagnosis of Dysgraphia Using Machine Learning Approach

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Abstract

Dysgraphia is a handwriting problem that impairs a person’s ability to write. Even the diagnosis of this condition is challenging, and there is currently no cure. Researchers from all over the world have studied this issue and offered several solutions. Motivation to work on this problem did arise after meeting with a few students struggling in achieving performance despite putting in sincere efforts. This paper also discusses the various forms of dysgraphia and its associated symptoms and proposes machine-learning models to detect dysgraphia. Unsupervised machine learning techniques are used to detect dysgraphia-related handwriting impairment. To accomplish the goal, a fresh handwriting dataset is created by conducting handwriting exercises and a wide variety of features are extracted to represent various handwriting characteristics. Results indicate that Random forest returns the best accuracy but scores less while detecting dysgraphic samples correctly. One class SVM has been tried to deal with the issue of the availability of dysgraphic samples required to train machines. Results indicate good hope in identification with a scope of improvement with increase in sample size for machine training. This paper also seeks to raise awareness of the dysgraphia issue and its effects on society.

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Data availability

The data used in the paper are curated by the authors themselves with permission from the schools and parents. The data will be made available to the interested researchers on request from the first author.

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Acknowledgements

This research work has been carried out with the funding from the Project No SEED/TIDE/2019/705. The authors gratefully acknowledge the funding support received from Technology Interventions for Disabled and Elderly (TIDE) scheme under the Department of Science and Technology (DST).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Central University of Rajasthan, Ajmer, India

    Basant Agarwal

  2. PG Department of Computer Science and Technology, Sardar Patel University, Vallabh Vidhyanagar, India

    Sonal Jain

  3. Department of Computer Science and Engineering, Indian Institute of Information Technology Kota, Kota, India

    Kishan Beladiya, Yash Gupta & Avijit Singh Yadav

  4. Department of Computer Science, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248007, India

    Neelu Jyothi Ahuja

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  1. Basant Agarwal
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  2. Sonal Jain
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  3. Kishan Beladiya
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  4. Yash Gupta
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  5. Avijit Singh Yadav
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  6. Neelu Jyothi Ahuja
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Correspondence to Basant Agarwal.

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This article is part of the topical collection “Industrial IoT and Cyber-Physical Systems” guest edited by Arun K Somani, Seeram Ramakrishnan, Anil Chaudhary and Mehul Mahrishi.

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Agarwal, B., Jain, S., Beladiya, K. et al. Early and Automated Diagnosis of Dysgraphia Using Machine Learning Approach. SN COMPUT. SCI. 4, 523 (2023). https://doi.org/10.1007/s42979-023-01884-0

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