Handwritten Digits Recognition Technology Based on SAE-SVM Classifier

DOI

10.2991/ameii-16.2016.251How to use a DOI?

Keywords

Handwritten Digits Recognition, Stacked Auto Encoder, BP Algorithm

Abstract

For the purpose of this article, there is a huge amount of work in feature extraction, selection and other aspects using traditional supervised machine learning methods, and features for different applications often required different scenarios which need to manually design, but with the final result not ideal. The paper shows a unsupervised feature extraction method-combine Stacked Auto Encoder and Support Vector Machine, experiments had shown that the algorithm's accuracy is 99.31% in MINIST better than other algorithms. This study can help Handwritten Digits Recognition get better development in various fields, such as ZIP code automatic identification, automatic processing of bank checks.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoting Du
PY  - 2016/04
DA  - 2016/04
TI  - Handwritten Digits Recognition Technology Based on SAE-SVM Classifier
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.251
DO  - 10.2991/ameii-16.2016.251
ID  - Du2016/04
ER  -