• DocumentCode
    1598468
  • Title

    The recognition of electronic component´s printed marks based on rough set

  • Author

    Guosen ; Wang, Yan ; Wu, Zhi-cheng

  • Author_Institution
    Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
  • Volume
    2
  • fYear
    2004
  • Firstpage
    1116
  • Abstract
    Rough set (RS) developed by Professor Z. Pawlak in 1982 has been applied successfully in such fields as machine learning, data mining, intelligent data analyzing, control algorithm acquiring, etc. In our paper, we apply this theory to recognize characters printed on the surface of electronic component. According to the properties of these characters, we propose a new method based on mathematical morphology to extract feature. Here, we propose a new algorithm to generate rules from decision table, and classify character based on the prior probability derived from such rule set In order to reduce the complexity further, we design a flow to pick train samples. The result of experiment shows our methods are useful and efficiency.
  • Keywords
    character recognition; decision tables; feature extraction; image sampling; knowledge acquisition; learning (artificial intelligence); mathematical morphology; printed circuits; rough set theory; decision table; electronic component; feature extraction; mathematical morphology; printed character recognition; probability; rough set theory; rule generation; train sample; Algorithm design and analysis; Character recognition; Data analysis; Data mining; Intelligent control; Learning systems; Machine learning; Machine learning algorithms; Rough surfaces; Surface roughness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Technology, 2004. IEEE ICIT '04. 2004 IEEE International Conference on
  • Print_ISBN
    0-7803-8662-0
  • Type

    conf

  • DOI
    10.1109/ICIT.2004.1490234
  • Filename
    1490234