IDEAS home Printed from https://ideas.repec.org/a/alk/jcsts0/v2y2020i2id529.html

Pakistani Currency Recognition to Assist Blind Person Based on Convolutional Neural Network

Author

Listed:
  • Muhammad Imad

    (Department of Computing and Technology Abasyn University, Peshawar, Pakistan)

  • Farhat Ullah

    (Department of Computing and Technology, Abasyn University, Peshawar, Pakistan)

  • Muhammad Abul Hassan

    (Department of Computing and Technology, Abasyn University, Peshawar, Pakistan)

  • Naimullah

    (Department of Computing and Technology, Abasyn University, Peshawar, Pakistan)

Abstract

A visually impaired person faces many difficulties in their daily life, such as having trouble finding their ways, recognize the person and objects. One of the crucial problems is to recognize the currencies for a blind or visually impaired person. In this research article, we have proposed a system to recognize a Pakistani currency for a blind or visually impaired person based on Convolutional Neural Network (CNN) and Support Vector Machine (SVM). In the proposed system, seven different Pakistani paper currency notes (Rs.10, 20, 50, 100,500, 1000 and 5000) are used for training and testing. Experimental results show that the proposed system can recognize seven notes of Pakistan's Currency (Rs. 10, 20, 50, 100, 500, 1000, 5000) successfully with an accuracy of 96.85%.

Suggested Citation

  • Muhammad Imad & Farhat Ullah & Muhammad Abul Hassan & Naimullah, 2020. "Pakistani Currency Recognition to Assist Blind Person Based on Convolutional Neural Network," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 2(2), pages 12-19, October.
    • Handle: RePEc:alk:jcsts0:v:2:y:2020:i:2:id:529
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/view/529
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jcsts/article/download/529/488
    File Function: Full text
    Download Restriction: no
    ---><---

    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:alk:jcsts0:v:2:y:2020:i:2:id:529. 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: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .

    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.