<![CDATA[Handwritten digit recognition is an important component of intelligent educational interfaces, particularly in early learning, digital assessment, and handwriting feedback systems. However, many deep learning-based recognition models rely on large benchmark datasets, whereas practical educational environments may involve limited and heterogeneous learner-generated samples. This study presents a technical feasibility analysis of a hybrid handwritten-digit recognition framework that combines VGG19-based deep feature extraction, XGBoost classification, and Structural Similarity Index Measure (SSIM)-based visual-similarity feedback. A small handwritten-digit dataset was used to simulate a constrained-data setting, with augmentation applied only to the training data to reduce the risk of overfitting. The proposed VGG19-XGBoost pipeline was evaluated against baseline CNN-based models using accuracy, precision, recall, and F1-score, with SSIM used as a supplementary metric to assess the structural similarity between learner input and reference digit forms. The experimental results indicate that the hybrid approach provides stable preliminary classification performance under limited-data conditions and that SSIM can support interpretable visual feedback for handwriting evaluation. However, due to the small number of original samples, the findings should be interpreted as evidence of feasibility rather than as generalizable performance claims. Future work should involve larger real-world datasets, teacher-validated scoring rubrics, and deployment-oriented evaluation in digital learning environments.]]>
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