MNIST Handwritten Digit Classification Based on Convolutional Neural Network with Hyperparameter Optimization

Abstract

Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange structure, including 3 convolution and activation layers for feature extraction and 2 fully connected layers (i.e., dense layers) for classification. The model’s hyperparameters, such as the batch sizes, kernel sizes, batch normalization, activation function, and learning rate are optimized to enhance the recognition performance. The average classification accuracy of the proposed methodology is found to reach 99.82% on the training dataset and 99.40% on the testing dataset, making it a nearly error-free system for MNIST recognition.