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Author:

Wu, Peiqi (Wu, Peiqi.) | Huang, Zhangqin (Huang, Zhangqin.) (Scholars:黄樟钦) | Li, Da (Li, Da.)

Indexed by:

EI Scopus

Abstract:

In the process of Chinese license plate recognition, the main problems are as follows, such as the feature extraction method is cumbersome and inefficient, the Chinese character recognition rate is low. This paper studies the license plate technology in depth based on convolution neural network. In order to solve the problem of low recognition rate of Chinese characters, we use continuous convolution layers to convolve the image and extract more characters. Experiments show that the proposed method can more effectively extract the license plate characteristics, improve the license plate recognition rate. © 2017 IEEE.

Keyword:

Neural networks License plates (automobile) Optical character recognition Convolution

Author Community:

  • [ 1 ] [Wu, Peiqi]Beijing Advanced Innovation Center for Future Internet Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, China
  • [ 2 ] [Huang, Zhangqin]Beijing Advanced Innovation Center for Future Internet Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, China
  • [ 3 ] [Li, Da]Beijing Advanced Innovation Center for Future Internet Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, China

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Source :

Year: 2017

Volume: 2018-January

Page: 1652-1656

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 10

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