<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Aiming at the weakness of CNN that is not sensitive to the changes of relative position and angle, a method of digital handwritten recognition based on deep capsule network is researched. The capsule network represents multiple attributes of an entity through a group of capsules composed of neurons, which effectively preserves the information about the position and posture of the entity. Dynamic routing algorithm makes the information interaction between capsules more clearly, and can determine the pose of the entity more accurately. While solving the shortcomings of convolutional neural networks, it also integrates the advantages of CNN and considers the relative position of it’s lack, so that the recognition effect is improved. The design implements a deep capsule network, reduces the amount of trainable parameters by changing the size of the convolution kernel, expands on the original network structure, adds a convolution after the convolution layer, and a process of dynamic routing on the main dynamic routing is added, and the number of iterations is changed for experimentation, which makes the accuracy of network recognition higher on the MNIST data set.</p></abstract>

Aiming at the weakness of CNN that is not sensitive to the changes of relative position and angle, a method of digital handwritten recognition based on deep capsule network is researched. The capsule network represents multiple attributes of an entity through a group of capsules composed of neurons, which effectively preserves the information about the position and posture of the entity. Dynamic routing algorithm makes the information interaction between capsules more clearly, and can determine the pose of the entity more accurately. While solving the shortcomings of convolutional neural networks, it also integrates the advantages of CNN and considers the relative position of it’s lack, so that the recognition effect is improved. The design implements a deep capsule network, reduces the amount of trainable parameters by changing the size of the convolution kernel, expands on the original network structure, adds a convolution after the convolution layer, and a process of dynamic routing on the main dynamic routing is added, and the number of iterations is changed for experimentation, which makes the accuracy of network recognition higher on the MNIST data set.

Deep Capsule Network Handwritten Digit Recognition

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Deep Capsule Network Handwritten Digit Recognition"}

Aiming at the weakness of CNN that is not sensitive to the changes of relative position and angle, a method of digital handwritten recognition based on deep...

Deep Capsule Network Handwritten Digit Recognition

Data publikacji: 11 sty 2021

Zakres stron: 1 - 8

DOI: https://doi.org/10.21307/ijanmc-2020-031

Słowa kluczowe
Component, Deeplearning, Nerve Capsule, Deep Capsule Network, Handwritten Digit Recognition

© 2020 Yuxing Tan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Deep Capsule Network Handwritten Digit Recognition

Data publikacji: 11 sty 2021

Zakres stron: 1 - 8

DOI: https://doi.org/10.21307/ijanmc-2020-031

Słowa kluczoweComponent, Deeplearning, Nerve Capsule, Deep Capsule Network, Handwritten Digit Recognition

© 2020 Yuxing Tan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Component, Deeplearning, Nerve Capsule, Deep Capsule Network, Handwritten Digit Recognition