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DOI: 10.14569/SpecialIssue.2011.010108
PDF

ID Numbers Recognition by Local Similarity Voting

Author 1: Shen Lu
Author 2: Yanyun Qu
Author 3: Yanyun Cheng
Author 4: Yi Xie

International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Image Processing and Analysis, 2011.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: This paper aims to recognize ID numbers from three types of valid identification documents in China: the first-generation ID card, the second-generation ID card and the driver license of motor vehicle. We have proposed an approach using local similarity voting to automatically recognize ID numbers. Firstly, we extract the candidate region which contains ID numbers and then locate the numbers and characters. Secondly, we recognize the numbers by an improved template matching method based on the local similarity voting. Finally, we verify the ID numbers and characters. We have applied the proposed approach to a set of about 100 images which are shot by conventional digital cameras. The experimental results have demonstrated that this approach is efficient and is robust to the change of illumination and rotation. The recognition accuracy is up to 98%.

Keywords: template matching algorithm; ID number recognition; OCR

Shen Lu, Yanyun Qu, Yanyun Cheng and Yi Xie, “ID Numbers Recognition by Local Similarity Voting” International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Image Processing and Analysis, 2011. http://dx.doi.org/10.14569/SpecialIssue.2011.010108

@article{Lu2011,
title = {ID Numbers Recognition by Local Similarity Voting},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Image Processing and Analysis}
doi = {10.14569/SpecialIssue.2011.010108},
url = {http://dx.doi.org/10.14569/SpecialIssue.2011.010108},
year = {2011},
publisher = {The Science and Information Organization},
volume = {1},
number = {1},
author = {Shen Lu and Yanyun Qu and Yanyun Cheng and Yi Xie},
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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