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Multi-type Digital Recognition Based on TensorFlow

  • Qingdao University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

For the problem of inaccurate identification of numbers, this paper is based on LeNet-5 network structure and optimizes it. The objective function and optimizer are added after the network output, and the sample library is updated to make it more accurate to identify multiple types of numbers. The optimized architecture is applied to identify multiple types of numbers, trained and tested, and the optimization parameters are selected by comparison. The experimental results show that the optimization parameters of certain values have a higher recognition rate for identifying numbers. The study has reference value for multi-type digital recognition.

Original languageEnglish
Title of host publicationProceedings 2018 Chinese Automation Congress, CAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1983-1985
Number of pages3
ISBN (Electronic)9781728113128
DOIs
StatePublished - 2 Jul 2018
Externally publishedYes
Event2018 Chinese Automation Congress, CAC 2018 - Xi'an, China
Duration: 30 Nov 20182 Dec 2018

Publication series

NameProceedings 2018 Chinese Automation Congress, CAC 2018

Conference

Conference2018 Chinese Automation Congress, CAC 2018
Country/TerritoryChina
CityXi'an
Period30/11/182/12/18

Keywords

  • LeNet-5 model
  • convolutional neural networks
  • multiple types of numbers

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