• DocumentCode
    1578855
  • Title

    An evaluation on offline signature verification using artificial neural network approach

  • Author

    O-Khalifa, Othman ; Alam, M.K. ; Abdalla, A.H.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia
  • fYear
    2013
  • Firstpage
    368
  • Lastpage
    371
  • Abstract
    The signature verification is the oldest security technique to verify the identification of persons. Recently, the signature recognition schemes are growing in the world of security technology. It offers two different types of schemes those are offline and online method. The offline technique means to verify a signature written on paper which is scanned to convert it into a digital image, whereas the online system required an online device such as Tablet PC, touch screen monitor by a pressure sensitive pen to verify the signature. This paper discusses a review of offline signature verification schemes which considered as a highly secured technique to recognize the genuine person´s identity. It addresses the offline signature verification technique using Artificial Neural Network (ANN) approach. It also explains the fundamental characteristics of offline signature verification processes and highlights the comparison among various offline signature verification approaches and various signature recognition issues.
  • Keywords
    handwriting recognition; neural nets; ANN approach; artificial neural network approach; offline signature verification; security technique; signature recognition schemes; Artificial neural networks; Databases; Digital images; Feature extraction; Forgery; Artificial Neural Network; Features extraction and Forgeries; Offline Signature Verification; Preprocessing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on
  • Conference_Location
    Khartoum
  • Print_ISBN
    978-1-4673-6231-3
  • Type

    conf

  • DOI
    10.1109/ICCEEE.2013.6633964
  • Filename
    6633964