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    Automated Bilingual Handwriting Evaluation for Early Childhood Education: A Multi-Metric Structural Analysis
    (Institute of Electrical and Electronics Engineers Inc, 2026-07-02) Gunawardena, T; Rathnasinghe, E; Abesinghe, C; Mihara, B; Abeywardhana, L; Weerasinghe, L; Selvaratnam, N
    Traditional handwriting assessment in early childhood education relies on subjective teacher evaluation and becomes particularly challenging in bilingual Sri Lankan curricula involving both English and Sinhala. This paper introduces a multi-metric handwriting quality scoring pipeline for automated evaluation of preschool handwritten letters for children aged 4-6. The proposed system integrates contour-based segmentation, language-specific convolutional neural network (CNN)-based recognition (achieving 86.73% test accuracy on EMNIST for English and 99.04% on a custom Sinhala dataset), and an eight-metric structural analysis framework. The framework combines structural similarity (SSIM), topology preservation, shape invariants, proportion analysis, stroke continuity, alignment, pixel Intersection over Union (IoU), and a 9 × 9 grid-based spatial comparison. The weighted scoring approach shows strong agreement with teacher assessments, demonstrating the reliability of the proposed system. Designed for Sri Lankan early education contexts, the approach provides a scalable, interpretable, and objective solution, addressing the gap in automated Sinhala handwriting assessment tools.
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    Multimodal Knowledge Graph for Domain-Specific Intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2025) Mohan, K; Munasinghe, M; Bandara, L; Wijesinghe, H; Rathnayake, S; Abeywardhana, L
    In the era of information abundance, transforming vast amounts of data into meaningful knowledge remains a critical challenge, especially in domains like medicine, engineering, and education, where visual and multimodal elements play a vital role. Traditional Knowledge Graphs (KGs) excel in organizing structured and textual data but struggle to incorporate multimodal information and implicit relationships, limiting their effectiveness. This paper explores the potential of Multimodal Knowledge Graphs (MMKGs) to address these limitations by integrating text, images, videos, and audio into a unified framework. We investigate how MMKGs enhance knowledge retrieval, comprehension, and interactive learning through advanced techniques, including Natural Language Processing and deep learning. Our findings demonstrate that MMKGs significantly improve knowledge retention and application in specialized fields, offering a foundation for more intuitive and effective domain-specific knowledge ecosystems.