• Corpus ID: 41690405

A Survey on Counterfeit Paper Currency Recognition and Detection

@inproceedings{Mahajan2014ASO,
  title={A Survey on Counterfeit Paper Currency Recognition and Detection},
  author={Shital M. Mahajan and K. P. Rane},
  year={2014}
}
This surveys paper reports various articles dealing with counterfeit paper currency recognition and detection systems. This paper attempts to represent the survey on fake money detection because almost every country in the world is facing the problem of forged money, but in India, the problem is exasperating as the country is hit hard by this evil practices. Counterfeit notes of Rs.100, 500 and 1000 are being spread all over the world that’s why it is important to detect such fake notes which… 
Detection of Fake Indian Currency
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This project will recognize Indian currency notes using a real time image obtained from a webcam using image processing technology and applying it for the purpose of verifying valid currency notes and detects fake currency by extracting features of notes.
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Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world.
Image processing based Feature extraction of Bangladeshi banknotes
  • Z. Ahmed, S. Yasmin, M. Islam, R. Ahmed
  • Computer Science
    The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)
  • 2014
TLDR
A core software system to build a robust automated counterfeit currency detection tool for Bangladeshi bank notes using OCR, Contour Analysis, Face Recognition, Speeded UP Robust Features (SURF) and Canny Edge & Hough transformation algorithm of OpenCV is presented.
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TLDR
The present survey covers a wide range of anti-counterfeiting security features, categorizing them into three components: security substrate, security inks and security printing respectively, and presents works in the literature covering these three categories.
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The proposed method feature extraction is based on the characteristics of Indian paper currencies and produced classification accuracy of 95.8%.
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In the paper, a system is proposed which is used to identify the old and soiled Indian paper currency notes. When the new currency notes are introduced and put into circulation, they get passed from
Bogus currency authorization using HSV techniques
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The main objective of this work is used to identify fake currencies among the real currency by using edge detection techniques, used to enhance reliability and dynamic way in detecting the counterfeit currency.
Fig . 3 Output Image III . PROPOSED WORK FLOW CHART : Fig . 4 Block diagram ALGORITHM : STEP 1
TLDR
In the proposed work, detection of the counterfeit banknotes using multispectral images is implemented by Neural Network technique, which will reduce the cost when neural networks are used and shows good performance and a low level of complexity.
INDIAN FAKE CURRENCY DETECTION USING COMPUTER VISION
TLDR
A computer vision based approach for Indian paper currency detection by using ORB and BF matcher in OpenCV based and the average accuracy of detection is up to 95.0% and tested this method on different denominations of Indian banknote.

References

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TLDR
An automa ed recognition of currency notes is introduced by with the help of feature extraction, classification based in SVM, Neural Net s, and heuristic approach to deal with fake notes in India.
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TLDR
In this paper, the original infrared images have been embossed process firstly, then the binary images are gained by using closing operation, and the template matching is used to identify.
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TLDR
In this method, using only one intact example of paper currency from each denomination is enough for training the system, and the system was able to recognize 95% of data, correctly.
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TLDR
Experimental results show that this Neural Network based recognition scheme for Bangladeshi banknotes can recognize currently available 8 notes successfully with an average accuracy of 98.57%.
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TLDR
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TLDR
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TLDR
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TLDR
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