Analysis of two handwritten digit recognition methods based on neural network

In the field of image recognition, Convolutional neural networks (CNNs) and Fully connected neural networks (FCNNs) are two commonly used deep learning models. For the handwritten digit recognition task, the researchers compared the performance of the two methods on the MNIST dataset. The performance of these models of two neural networks performing the same task on the same dataset will be included in this article. In this paper, the construction and operation process of a fully connected neural network and convolutional neural network are theoretically analyzed. The researchers used the MNIST handwritten digits dataset for their study. The results show that the convolutional neural network is superior to the fully connected neural network in recognition accuracy and time consumption. This is mainly attributed to the advantages of convolutional neural networks that can better retain spatial structure information, reduce the number of parameters, and share weights when processing image data. In contrast, fully connected neural networks need to process many parameters, and is difficult to effectively extract image features. Therefore, when using the MNIST dataset as an image recognition task, researchers believe that using convolutional neural networks is a more effective and efficient method.