Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

Handwritten Digits Recognition Technology Based on SAE-SVM Classifier

Authors
Xiaoting Du
Corresponding Author
Xiaoting Du
Available Online April 2016.
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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Volume Title
Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
Series
Advances in Engineering Research
Publication Date
April 2016
ISBN
978-94-6252-188-9
ISSN
2352-5401
DOI
10.2991/ameii-16.2016.251How to use a DOI?
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/).

Cite this article

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  -