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  • 标题:Best Combination of Binarization Methods for License Plate Character Segmentation
  • 本地全文:下载
  • 作者:Yoon, Youngwoo ; Ban, Kyu-Dae ; Yoon, Hosub
  • 期刊名称:ETRI Journal
  • 印刷版ISSN:1225-6463
  • 电子版ISSN:2233-7326
  • 出版年度:2013
  • 卷号:35
  • 期号:3
  • 页码:491-500
  • DOI:10.4218/etrij.13.0112.0545
  • 语种:English
  • 出版社:Electronics and Telecommunications Research Institute
  • 摘要:A connected component analysis from a binary image is a popular character segmentation method but occasionally fails to segment the characters owing to image noise and uneven illumination. A multimethod binarization scheme that incorporates two or more binary images is a novel solution, but selection of binarization methods has never been analyzed before. This paper reveals the best combination of binarization methods and parameters and presents an in-depth analysis of the multimethod binarization scheme for better character segmentation. We carry out an extensive quantitative evaluation, which shows a significant improvement over conventional single-method binarization methods. Experiment results of six binarization methods and their combinations with different test images are presented.
  • 关键词:Character segmentation;binarization;binary image;automatic license plate recognition
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