IDEAS home Printed from https://ideas.repec.org/a/epw/ejeng0/v7y2022i4id62851.html

Offline Handwritten Signature Recognition Using Deep Convolution Neural Network

Author

Listed:
  • Quazi Saad-ul Mosaher
  • Mousumi Hasan

Abstract

In the modern age, technological advancement reached a new limit where authentication plays a vital role in security management. Biometric-based authentication is the most referenced procedure for authentication where signature verification is a significant part of it for authentication of a person. To prevent the falsification of signatures on important documents & legal transactions it is necessary to recognize a person's signature accurately. This paper focused on recognizing offline handwritten original & forged signatures using a deep convolution neural network. We use a completely new dataset & also downloaded datasets to train the system & verify a random signature as genuine or forgery. All testing samples are collected from several individuals after several steps of preprocessing the model is fed with the resultant image to our system, the experimental results give us an accuracy of 95.5% from the dataset.

Suggested Citation

  • Quazi Saad-ul Mosaher & Mousumi Hasan, 2022. "Offline Handwritten Signature Recognition Using Deep Convolution Neural Network," European Journal of Engineering and Technology Research, European Open Science, vol. 7(4), pages 44-47, July.
    • Handle: RePEc:epw:ejeng0:v:7:y:2022:i:4:id:62851
    DOI: 10.24018/ejeng.2022.7.4.2851
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejeng/article/view/62851
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejeng/article/download/62851/12831
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejeng.2022.7.4.2851?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:ejeng0:v:7:y:2022:i:4:id:62851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejeng .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.