Research on a handwritten character recognition algorithm based on an extended nonlinear kernel residual network

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成果类型：

期刊论文

作者：

Rao, Zheheng;Zeng, Chunyan;Wu, Minghu*;Wang, Zhifeng;Zhao, Nan;...

通讯作者：

Wu, Minghu

作者机构：

[Zhao, Nan; Rao, Zheheng; Wu, Minghu; Liu, Min; Wan, Xiangkui; Zeng, Chunyan] Hubei Univ Technol, Hubei Key Lab High Efficiency Utilizat Solar Ener, Wuhan, Hubei, Peoples R China.

[Zhao, Nan; Rao, Zheheng; Wu, Minghu; Liu, Min; Wan, Xiangkui; Zeng, Chunyan] Hubei Univ Technol, Hubei Collaborat Innovat Ctr High Efficiency Util, Wuhan 430068, Hubei, Peoples R China.

[Wang, Zhifeng] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Hubei, Peoples R China.

通讯机构：

[Wu, Minghu] H

Hubei Univ Technol, Hubei Key Lab High Efficiency Utilizat Solar Ener, Wuhan, Hubei, Peoples R China.

Hubei Univ Technol, Hubei Collaborat Innovat Ctr High Efficiency Util, Wuhan 430068, Hubei, Peoples R China.

语种：

英文

关键词：

unsupervised priori algorithm;"high-capacity" convolution layers;residual network;multi-level quick link;Dropout layer

期刊：

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS

ISSN：

1976-7277

年：

2018

卷：

期：

页码：

413-435

DOI：

10.3837/tiis.2018.01.020

基金类别：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61471162, 61501178, 61501199, 61571182]; Program of International science and technology cooperation [2015DFA10940]; Science and technology support program (R & D) project of Hubei Province [2015BAA115]; PhD Research Startup Foundation of Hubei University of Technology [BSQD13037, BSQD14028]; Open Foundation of Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy [HBSKFZD2015005, HBSKFTD2016002]

机构署名：

本校为其他机构

院系归属：

教育信息技术学院

摘要：

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Des...

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Research on a handwritten character recognition algorithm based on an extended nonlinear kernel residual network

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