• DocumentCode
    1583003
  • Title

    Form document identification using line structure based features

  • Author

    Fan, Kuo-Chin ; Wang, Yuan-Kai ; Chang, Mei-Lin

  • Author_Institution
    Inst. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-Li, Taiwan
  • fYear
    2001
  • fDate
    6/23/1905 12:00:00 AM
  • Firstpage
    704
  • Lastpage
    708
  • Abstract
    Form recognition is one of the special applications of document analysis (DA). We present a novel form recognition method by analyzing the line structure embedded in an input form document. First, all vertical and horizontal lines embedded in the form image are extracted. By analyzing the crossing relationships among horizontal lines and vertical lines, a line crossing relationship matrix can be built with each row corresponding to one horizontal line and each column corresponding to one vertical line. Moreover two line distance relationship matrices, horizontal and vertical line distance relationship matrices, are built by analyzing the distance relationships among horizontal lines and vertical lines, respectively. Last, the recognition task is performed by matching these three matrices. Experimental results reveal the feasibility and efficiency of our proposed method in recognizing form documents
  • Keywords
    business forms; document image processing; image matching; optical character recognition; OCR; business form recognition; document analysis; document image recognition; experimental results; form document identification; horizontal lines; line crossing relationship matrix; line distance relationship matrices; line structure based features; vertical lines; Application software; Character recognition; Computer science; Image storage; Information analysis; Office automation; Optical devices; Pattern recognition; Storage automation; Text analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7695-1263-1
  • Type

    conf

  • DOI
    10.1109/ICDAR.2001.953881
  • Filename
    953881