Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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Author(s)

  • Fei Xie
  • Jing Zhao
  • Rui Sun
  • Lei Zhang
  • Yue Zhang

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Detail(s)

Original languageEnglish
Article number012022
Journal / PublicationJournal of Physics: Conference Series
Volume1004
Online published25 Apr 2018
Publication statusPublished - 2018

Conference

Title2nd International Conference on Machine Vision and Information Technology, CMVIT 2018
PlaceHong Kong
Period23 - 25 February 2018

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Abstract

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

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