Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch

Abstract

Thanks to advancements in machine learning and artificial intelligence techniques, computers can now practice on data and learn from it in a manner that is similar to how the human brain works. Handwritten character and number identification has been one of the most pressing and fascinating subjects in pattern recognition and image processing. One of the most urgent and intriguing topics in pattern recognition and picture processing has been the identification of handwritten characters and numbers. As a crucial part of artificial intelligence, handwritten digit identification technology provides a vast array of application possibilities. The data demonstrates that, even though handwritten numbers are simply created with a few straightforward strokes, the appearance of numbers is more variable due to the various writing styles of each person. In this study, a deep learning framework-based upgraded LeNet-5 convolutional neural network model is used to build a handwritten number recognition model in Python. Automatic recognition of handwritten numbers will become the standard recognition technique if it can be applied to a wide range of industries, including banking and accounting, and hence save human costs.