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Akshar Mitra: a multimodal integrated framework for early dyslexia detection
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DOI：10.3389/fdgth.2025.1726307.png)
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Abstract
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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.
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