Enhancing Image Recognition on MNIST Dataset Through VGG16 in CNN

Abstract

In discipline to improve image recognition on the Modified National Institute of Standard and Technology (MNIST) dataset a popular benchmark for handwritten digit classification this research study presents a novel method. The study uses the Visual Vector Geometry Group 16 (VGG16) a Convolutional Neural Network (CNN) architecture, which was primarily created for the ImageNet dataset, to increase classification accuracy on MNIST by utilizing potentiality of transfer learning. The document systematically describes the tools and techniques, including how to preprocess data, build models with TensorFlow and Keras, and modify MNIST for VGG16 step-by-step. The results show that MNIST data can be successfully aligned with VGG16 requirements, demonstrating the capability of transfer learning to enhance model performance. The paper highlights the importance of transfer learning in utilizing characteristics identified in a varied dataset such as ImageNet and habituating the features of handwritten digits in MNIST. Insights into the research's broader implications are provided in the study's conclusion, paving up possibilities for more studies on CNN optimization for handwritten digit recognition tasks. The methodologies and outcomes provided in this article provides insightful information and lay the foundation for further investigation on improving the efficiency and accuracy of image recognition. Overall, this research offers a convincing viewpoint on the link between deep learning and image classification by illuminating efficiency of the transfer learning with VGG16 in improving image reconition capabilities on the MNIST dataset.