• DocumentCode
    763752
  • Title

    Financial document processing based on staff line and description language

  • Author

    Tang, Yuan Y. ; Suen, Ching Y. ; De Yan, Chang ; Cheriet, Mohamed

  • Author_Institution
    Centre for Pattern Recognition & Machine Intelligence, Concordia Univ., Montreal, Que., Canada
  • Volume
    25
  • Issue
    5
  • fYear
    1995
  • fDate
    5/1/1995 12:00:00 AM
  • Firstpage
    738
  • Lastpage
    754
  • Abstract
    Millions of financial transactions take place every day. Associated with them are documents such as bank cheques, payment slips and bills which have to be processed. A great deal of time, effort and money will be saved if they can be entered into the computer and processed automatically. According to the specific characteristics of financial documents, it can be concluded that it is possible to build a system for recognizing specific types of financial documents, instead of a complex and general one aiming at different kinds of documents. In this paper, a financial document recognition prototype system which can process bank cheques, payment slips and bills, is presented. It consists of four major parts: (a) document image acquisition including scanning and binarization, (b) fixed document processing subsystem based on the detection of staff lines, (c) flexible document processing subsystem operating in a form description language (FDL), and (d) character recognition. Numerous experimental results are presented and discussed
  • Keywords
    document image processing; financial data processing; bank cheques; bills; binarization; character recognition; description language; document image acquisition; financial document processing; financial transactions; payment slips; scanning; staff line; Artificial intelligence; Automation; Character recognition; Graphics; Machine intelligence; Manuals; Marine vehicles; Pattern recognition; Prototypes; Writing;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9472
  • Type

    jour

  • DOI
    10.1109/21.376488
  • Filename
    376488