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Identification of Forged money using neural network and computer vision

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Aim: currency note identification using wavelet transform analysis

 

Abstract:

This paper propose an image processing technique to extract paper currency denomination .Automatic detection and recognition of Indian currency note has gained a lot of research attention in recent years particularly due to its vast potential applications. It is shown that Indian currencies can be classified based on a set of unique non discriminating features. First we acquire the image by simple flat scanner on fix dpi with a particular size, the pixels level is set to obtain mage. The dominant colour and the aspect ratio of the note are extracted. After this extracted the portion of the note containing the unique shape, number, emblem, etc. This technique is used to match or find currency denomination of paper currency.

 

Proposed system:

Discrete Wavelet Transform is applied on each currency note. The approximate coefficient matrix of the transformed image is derived .Next, a set of statistical features such as mean, standard deviation; skewness and kurtosis are extracted from the approximate coefficient matrix. The extracted features can be used for recognition, classification and retrieval of currency notes.

 

Block diagram:

Rupees detection:

 

Fake detection:

 

Advantages:

  • Accuracy is more
  • Less distortion rate

 

 Applications:

  • Authentication Purpose
  • Duplicate Identification

 

Software Requirements:

 

  • Python idle
  • Opencv
  • Numpy
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  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

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