  Although the image recognition has been a research topic for many years, many
researchers still have a keen interest in it[1]. In some papers[2][3][4],
however, there is a tendency to compare models only on one or two datasets,
either because of time restraints or because the model is tailored to a
specific task. Accordingly, it is hard to understand how well a certain model
generalizes across image recognition field[6]. In this paper, we compare four
neural networks on MNIST dataset[5] with different division. Among them, three
are Convolutional Neural Networks (CNN)[7], Deep Residual Network (ResNet)[2]
and Dense Convolutional Network (DenseNet)[3] respectively, and the other is
our improvement on CNN baseline through introducing Capsule Network
(CapsNet)[1] to image recognition area. We show that the previous models
despite do a quite good job in this area, our retrofitting can be applied to
get a better performance. The result obtained by CapsNet is an accuracy rate of
99.75\%, and it is the best result published so far. Another inspiring result
is that CapsNet only needs a small amount of data to get excellent performance.
Finally, we will apply CapsNet's ability to generalize in other image
recognition field in the future.
