The research for license plate recognition using sub-image fast independent component analysis

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

Abstract

In order to solve the problem that current license plate recognition methods, such as template matching and neural network computing, which need a large number of samples and large amount of computation, this paper proposed a sub-image fast independent component analysis (SI-FastICA) method for plate recognition. It can obtain the local feature of the image with a small amount of computation. In order to obtain better recognition results, in the stage of character segmentation, this paper carried segmentation based on the proposed relative coordinate dichotomy. Then, the feature of characters was extracted by SI-FastICA. The experiments show that SI-FastICA can reflect the local characteristics of the character very well. At last, this paper put the collected actual license plate images into experiment, and achieved good recognition results.

Original languageEnglish
Title of host publicationProceedings of the 2011 Chinese Control and Decision Conference, CCDC 2011
Pages1915-1920
Number of pages6
DOIs
StatePublished - 2011
Event2011 Chinese Control and Decision Conference, CCDC 2011 - Mianyang, China
Duration: 23 May 201125 May 2011

Publication series

NameProceedings of the 2011 Chinese Control and Decision Conference, CCDC 2011

Conference

Conference2011 Chinese Control and Decision Conference, CCDC 2011
Country/TerritoryChina
CityMianyang
Period23/05/1125/05/11

Keywords

  • character segmentation
  • difference projection
  • fast independent component analysis
  • plate recognition

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