A Novel Deep Convolutional Neural Network Structure for Off-line Handwritten Digit Recognition-Reference-Cited by-同舟云学术

A Novel Deep Convolutional Neural Network Structure for Off-line Handwritten Digit Recognition

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Big Data Technologies - ICBDT2019
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Author:

Wen Yan¹,Shao Yi²,Zheng Dabo³

Affiliation:

1. School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, China, Institute of Intelligent Machine, Chinese Academy of Sciences, Hefei, China

2. School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, China

3. SAIC General Motors Norsom Co. Ltd., Shenyang, China

Funder

The Key scientific research project of Suzhou University

Research platform project of Suzhou University

Major science and technology projects of Anhui

Key Course of Suzhou University

Publisher

ACM Press

Reference27 articles.

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2. Van Breukelen, M., Duin, R., Tax, D., Den Hartog, J. 1998. Handwritten digit recognition by combined classifiers. Kybernetika. 34, 4 (Jul. 1998), 381--386.

3. Qacimy B., Hammouch A., Kerroum M. 2015. A review of feature extraction techniques for handwritten Arabic text recognition. In Proceedings of the 2015 International Conference on Electrical and Information Technologies (Marrakech, Morocco, March 25-27, 2015). ICEIT '15.IEEE, Piscataway, NJ, 241--245. DOI= 10.1109/EITech.2015.7162979

4. Van Kessenich, L. M., Berger, D., De Arcangelis, L., Herrmann, H. J. 2019. Pattern recognition with neuronal avalanche dynamics. Phys. Rev. E. 99, 1(Jan. 2019), (010302) 1-6. DOI=https://doi.org/10.1103/PhysRevE.99.010302

5. Chen, Y., Xu, Z., Cai, S., Lang, Y., Jay, K. 2018. A saak transform approach to efficient, scalable and robust handwritten digits recognition. In Proceedings of the IEEE 2018 Picture Coding Symposium (San Francisco, CA, June 24-27, 2018). PCS '18. IEEE, Piscataway, NJ, 174--178. DOI=10.1109/PCS.2018.8456277.

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