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A Deep Network with Composite Residual Structure for Handwritten Character Recognition

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Advances in Internetworking, Data & Web Technologies (EIDWT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

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Abstract

This paper presents a new deep network (non – very deep network) with composite residual for handwritten character recognition. The main network design is as follows: (1) Introduces an unsupervised FCM clustering algorithm to preprocess the experimental data. (2) By exploiting a composite residual structure the multilevel shortcut connection is proposed which is more suitable for the learning of residual. (3) In order to solve the problem of overfitting and time-consuming for training the network parameters, a dropout layer is added after the completion of all convolution operations of each extended nonlinear residual kernel. Comparing with general deep network structures of same deep on handwritten character MNIST database, the proposed algorithm shows better recognition accuracy and higher recognition efficiency.

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Acknowledgments

This research was supported by National Natural Science Foundation of China (No. 61471162, No. 61501178, No. 61501199, No. 61601177); 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 (No. BSQD13037, No. BSQD14028); Open Foundation of Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy (HBSKFZD2015005, HBSKFTD2016002).

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Authors and Affiliations

  1. Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China

    Zheheng Rao, Chunyan Zeng, Nan Zhao, Min Liu & Minghu Wu

  2. Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, China

    Zheheng Rao, Chunyan Zeng, Nan Zhao, Min Liu & Minghu Wu

  3. School of Educational Information Technology, Central China Normal University, Wuhan, 430079, China

    Zhifeng Wang

Authors
  1. Zheheng Rao
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  2. Chunyan Zeng
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  3. Nan Zhao
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  4. Min Liu
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  5. Minghu Wu
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  6. Zhifeng Wang
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Corresponding author

Correspondence to Minghu Wu .

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Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Rao, Z., Zeng, C., Zhao, N., Liu, M., Wu, M., Wang, Z. (2018). A Deep Network with Composite Residual Structure for Handwritten Character Recognition. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

  • eBook Packages: EngineeringEngineering (R0)

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