Development of a conditional variational autoencoder for handwritten digit recognition.

Abstract

This study examines the performance of Conditional Variational Autoencoder (CVAE) in handwritten digit recognition. Using the MNIST dataset, two variants of the CVAE models — convolutional and multilevel architecture — were developed and compared. The research methodology includes comprehensive data preprocessing, architecture design, training, and thorough evaluation processes. The obtained data highlight the better performance of the convolutional model-based CVAE in achieving recognition accuracy compared to its multilayer counterpart. Evaluation metrics include analysis of original and reconstructed images, comparison of hidden layer vector distribution patterns, and visualization of loss function dynamics. In addition, the study highlights the practical implications of CVAEs in various fields, highlighting their performance in digit recognition tasks due to their inherent robustness and extraordinary generalizability.