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In order to address dysgraphia, a learning disorder that affects handwriting abilities, our research presents a novel web architecture. Leveraging machine learning and computer vision, our framework focuses on early and precise dysgraphia detection in students. Key to our approach is the thorough analysis of handwriting characteristics using advanced artificial intelligence algorithms. This not only facilitates swift and accurate dysgraphia identification but also offers crucial insights to educators, parents, and healthcare professionals. Our distinctive framework streamlines detection by integrating adaptive learning algorithms that tailor educational interventions to each student’s unique needs, enhancing overall support effectiveness. A standout feature is real-time processing of handwriting data, allowing continuous refinement and evolution of the detection mechanism for improved accuracy and efficiency.

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Building Comprehensive Web Framework Addressing Learning Disability Using Machine Learning and Computer Vision

  • 会议论文

  • 发表日期：

    2025年

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  • Pranil Patil,
  • Mihir Shrivas,
  • Tanishq Sahane,
  • Bhushan Patil,
  • Sameer Nanivadekar,
  • Shital Agrawal

摘要

In order to address dysgraphia, a learning disorder that affects handwriting abilities, our research presents a novel web architecture. Leveraging machine learning and computer vision, our framework focuses on early and precise dysgraphia detection in students. Key to our approach is the thorough analysis of handwriting characteristics using advanced artificial intelligence algorithms. This not only facilitates swift and accurate dysgraphia identification but also offers crucial insights to educators, parents, and healthcare professionals. Our distinctive framework streamlines detection by integrating adaptive learning algorithms that tailor educational interventions to each student’s unique needs, enhancing overall support effectiveness. A standout feature is real-time processing of handwriting data, allowing continuous refinement and evolution of the detection mechanism for improved accuracy and efficiency.