• DocumentCode
    1331281
  • Title

    Modified fractal signature (MFS): a new approach to document analysis for automatic knowledge acquisition

  • Author

    Tang, Yuan Y. ; Ma, Hong ; Xi, Dihua ; Mao, Xiaogang ; Suen, Ching

  • Author_Institution
    Dept. of Comput. Studies, Hong Kong Baptist Univ., Kowloon, Hong Kong
  • Volume
    9
  • Issue
    5
  • fYear
    1997
  • Firstpage
    747
  • Lastpage
    762
  • Abstract
    One of the key technologies related to knowledge and data engineering is the acquisition of knowledge and data in the development and utilization of information system and the strategies to capture new knowledge and data. Actually, millions of documents, including technical reports, government files, newspapers, books, magazines, letters, bank checks, etc., have to be processed every day, and knowledge has to be acquired from them. This paper presents a new approach to document analysis for automatic knowledge acquisition. The traditional approaches have two major disadvantages: (1) They are not effective for processing documents with high geometrical complexity. Specially, the top-down approach can process only the simple documents which have specific format or contain some a priori information. (2) The top-down approach needs to split large components into small ones iteratively, while the bottom-up approach needs to merge small components into large ones iteratively. They are time consuming. This new approach is based on modified fractal signature. It can overcome the above weaknesses
  • Keywords
    computational complexity; database management systems; document handling; knowledge acquisition; automatic knowledge acquisition; bottom-up approach; data engineering; document analysis; information system; knowledge acquisition; knowledge engineering; modified fractal signature; top-down approach; Books; Data engineering; Fractals; Government; Information analysis; Information systems; Knowledge acquisition; Knowledge engineering; Text analysis; Writing;
  • fLanguage
    English
  • Journal_Title
    Knowledge and Data Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1041-4347
  • Type

    jour

  • DOI
    10.1109/69.634753
  • Filename
    634753