<![CDATA[Handwritten digit recognition plays an important role in intelligent educational interfaces, early learning analytics, and human–computer interaction. This study investigates the technical feasibility of integrating transfer learning and gradient boosting for low-resource handwritten digit recognition environments relevant to emerging digital education platforms. A pre-trained VGG19 network is employed as a deep feature extractor, followed by classification using XGBoost. Structural Similarity Index (SSIM) analysis is incorporated as a feedback metric to quantify visual similarity between learner input and reference digit forms. The work is positioned as a feasibility validation of a hybrid vision-learning pipeline under limited-data conditions to explore its applicability in future AI-assisted handwriting support systems. Experiments are conducted using controlled augmentation, k-fold cross-validation, and comparative baseline CNN models. Results demonstrate stable classification behaviour and consistent SSIM-based similarity scoring, supporting the suitability of the architecture as a building block for adaptive digital handwriting evaluation systems. The framework demonstrates potential for integration into next-generation digital learning tools, including early education, special education support, skill assessment interfaces, and AI-driven formative feedback platforms.]]>
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