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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Fake currency detection using image processing method

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    13301148, 12101139, 12101142 & 11201016_CSE.pdf (990.5Kb)
    Date
    2016
    Publisher
    BRAC University
    Author
    Alimushwan, Munshi Md.
    Mohaimin, Akiful
    Islam, Rifat
    Chowdhury, Shahriar
    Metadata
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    URI
    http://hdl.handle.net/10361/7622
    Abstract
    Fake notes area problem of almost every country. For country like Bangladesh it is becoming big hurdle. Fake Bangladeshi Currency of 1000 seems to have flooded the system and there is no proper way to deal with them for a common person. Therefore this has led to the increase of corruption in our country hindering country’s growth. Common man became a scapegoat for the fake currency circulation, let us suppose that a common man went to a bank to deposit money in bank but only to see that some of the notes are fake, in this case he has to take the blame. Because of the advances in printing, scanning technologies it is easily possible for a person to print fake notes with use of latest hardware tools. Detecting fake notes manually becomes time consuming and untidy process hence there is need of automation techniques with which currency recognition process can be efficiently done. The issue of efficiently distinguishing counterfeit banknotes from genuine ones via automatic machines has become more and more important. However, The main objective of this project is fake currency detection using MatLab. This process can be automated in a computer using the application software. The basic logic is developed using Image acquisition, gray scale conversion ,edge detection, image segmentation, feature extraction and comparison. The magnified image of the original currency is fed to the Matlab database. The features of the note to be tested are compared with the dataset formed from the original magnified image and finds out whether it is fake or original. The most important challenge is systematically and methodologically repeating the analysis process to reduce human error and time.
    Keywords
    Image Acquisition; Gray scale conversion; Edge detection; Image segmentation; Feature extraction; Magnification; Comparison
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 34-35).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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