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Research Article | Open Access |

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 · Published 2011

DOI: 10.14569/SpecialIssue.2011.010108

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

How to Cite this Article

Lu, S., Qu, Y., Cheng, Y., & Xie, Y. (2011). ID Numbers Recognition by Local Similarity Voting. International Journal of Advanced Computer Science and Applications, 1(1). https://doi.org/10.14569/SpecialIssue.2011.010108

Lu, Shen, et al.. "ID Numbers Recognition by Local Similarity Voting." International Journal of Advanced Computer Science and Applications, vol. 1, no. 1, 2011, https://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},
  volume    = {1},
  number    = {1},
  year      = {2011},
  publisher = {The Science and Information Organization},
  note      = {Special Issue on Image Processing and Analysis},
  author    = {Shen Lu and Yanyun Qu and Yanyun Cheng and Yi Xie},
  doi       = {10.14569/SpecialIssue.2011.010108},
  url       = {https://doi.org/10.14569/SpecialIssue.2011.010108}
}

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