Handwritten Digit Recognition

Abstract

Project deals with the applications of ML (Machine Learning ) techniques for detecting Hand written digit classification, with many real-world applications such as digitizing historical documents, recognizing handwritten addresses on envelopes, and processing handwritten forms. In this project, we aimed to develop a machine learning model that can accurately identify and classify handwritten digits from an image. We trained our model on a dataset of handwritten digit images, the MNIST dataset, using convolutional neural network (CNN) architecture. Our preprocessing techniques included resizing, normalization, and augmentation. We evaluated our model on a separate set of test images and achieved an accuracy of 99.3 Our results demonstrate the effectiveness of CNN architecture for handwritten digit recognition, as well as the importance of preprocessing techniques in improving accuracy. We discuss potential areas for further research, such as exploring different CNN architectures or datasets, and the implications of our findings for real world applications. Overall, this project serves as an example of the potential of machine learning and computer vision to automate tasks and improve efficiency.