Gunawardena, TRathnasinghe, EAbesinghe, CMihara, BAbeywardhana, LWeerasinghe, LSelvaratnam, N2026-08-182026-07-02979-833155818-5https://rda.sliit.lk/handle/123456789/5238Traditional 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.encomputer visionconvolutional neural networks (CNNs)early childhood educationhandwriting evaluationmulti-metric scoringSinhala handwritingConference PaperDOI: 10.1109/ECAI69016.2026.11613754