Digit recognition in handwriting is a general issue of computer vision and pattern recognition, which is extensively used in postal systems, banking, and document processing. This paper provides a significant comparison between a developed Multilayer Perceptron (MLP) model and the other common machine learning and deep learning algorithms, such as a Convolutional Neural Network (CNN), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN). Some of the methods that have been included in the advanced MLP to improve its performance and minimize the risk of overfitting include dropout, batch normalization, learning rate scheduling, and early stopping. The models are tested on the MNIST dataset with such measures as accuracy, precision, recall, and F1-score. Experiments prove that CNNs are the most accurate since they have a higher quality of feature extraction, but the advanced MLP significantly performs better than other architectures and classical algorithms, which provides a robust and efficient solution to practical digit classification problems. The results highlight the significance of architectural contrivance and regularization designs to the enhancement of the neural network and give information on the model selection in the real-world implementations.
Velagala et al. (Wed,) studied this question.