  The MNIST dataset of the handwritten digits is known as one of the commonly
used datasets for machine learning and computer vision research. We aim to
study a widely applicable classification problem and apply a simple yet
efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate
the performance of the K-nearest neighbor classification algorithm on the MNIST
dataset where the $L2$ Euclidean distance metric is compared to a modified
distance metric which utilizes the sliding window technique in order to avoid
performance degradation due to slight spatial misalignments. The accuracy
metric and confusion matrices are used as the performance indicators to compare
the performance of the baseline algorithm versus the enhanced sliding window
method and results show significant improvement using this proposed method.
