English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, in order to improve the accuracy of handwritten number recognition, a convolutional neural network based on noise reduction and stack auto coding is formed by combining the denoising auto coding algorithm with stack auto coding algorithm. In this paper, the MNIST dataset is preprocessed first, then the noise reduction and stack auto coding network layer are constructed, and finally the stack auto coding algorithm based on the improved denoising auto coding is used to train and test the dataset. Experimental data show that the proposed method achieves low loss value and effectively improves the recognition accuracy in the MNIST test set.

Handwritten Digit Recognition Scheme Based on Noise Reduction and Trestle Self-Coding