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Akshar Mitra: a multimodal integrated framework for early dyslexia detection
Vibha Tiwari1,2, Ocean Agarwal2,3, Manya Sharma3
1Center for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Gwalior, India.
Insights
Akshar Mitra is a new framework for early dyslexia screening using eye-tracking, speech, and handwriting. This accessible, multimodal approach aims to bridge the global diagnostic gap for developmental dyslexia.
Area of Science:
- Neurobiology
- Computational Linguistics
- Educational Technology
Background:
- Developmental dyslexia affects 10-15% of children globally, often undiagnosed in resource-limited settings.
- Current screening methods rely on unimodal data and require large labeled datasets, limiting accessibility.
- Existing assessments are often inaccessible in resource-limited settings.
Purpose of the Study:
- To introduce Akshar Mitra, a novel Multimodal Integrated Framework (MMF) for accessible and early dyslexia screening.
- To integrate low-cost digital biomarkers from eye-tracking, speech, and handwriting analysis.
- To bridge the global dyslexia diagnostic gap through a scalable and explainable system.
Main Methods:
- Developed a Multimodal Integrated Framework (MMF) integrating three modules: webcam-based eye-tracking, automated speech assessment, and optical character recognition for handwriting.
- Extracted interpretable features (e.g., fixation regressions, word-error rate, character reversals) from each modality.
- Standardized features via a shared data schema and augmented with a behavioral questionnaire.
Main Results:
- The MMF successfully integrates multimodal data for a holistic dyslexia risk profile.
- The system provides objective, interpretable digital biomarkers for early screening.
- Incorporated user-friendly support tools, including a dyslexia-friendly reading interface.
Conclusions:
- Akshar Mitra offers a viable pathway to address the inaccessibility of conventional dyslexia assessments.
- The framework's scalable, language-agnostic design facilitates early detection and intervention.
- This transformative tool has the potential to improve educational outcomes for children with dyslexia worldwide.
Abstract:
Developmental dyslexia is a prevalent neurobiological disorder affecting 10%-15% of children globally, yet it remains largely undiagnosed due to the inaccessibility of conventional assessments in resource-limited settings. Existing screening methods are further constrained by their reliance on unimodal data streams and the need for large, clinically-labeled datasets. This paper presents Akshar Mitra, a Multimodal Integrated Framework (MMF), a novel computational methodology designed for accessible and early dyslexia screening. The framework pioneers the integration of three low-cost, high-yield digital biomarkers derived from eye-tracking, speech, and handwriting analysis.The MMF is implemented through three modules: webcam-based eye-tracking for fixation and saccadic analysis, automated speech assessment for fluency metrics, and optical character recognition for handwriting error detection. Each module extracts 4-6 interpretable features (e.g., fixation regressions, word-error rate, character reversals) that are standardized via a shared data schema. These objective measures are augmented by a concise behavioral questionnaire to generate a holistic risk profile. Beyond screening, the system incorporates support tools, including a dyslexia-friendly reading interface with syllable-level highlighting, to foster user engagement and confidence.By creating a scalable, language-agnostic, and explainable system, this work offers a viable pathway to bridge the global dyslexia diagnostic gap. The MMF provides a transformative tool for proactive screening, facilitating early intervention and improving educational outcomes.
