![sketchpen for fake notes sketchpen for fake notes](https://i.pinimg.com/originals/2b/ba/21/2bba21e1b8ef4949c6f6e6192995dba2.jpg)
The main focus of the method is feature extraction after performing the pre-segmentation procedure. Similarly, fake currency detection techniques developed by MATLAB follow the same general principle. By harvesting and classifying such features, we can develop new detection algorithms, which simply compare a computed input to an existing selected database. Various methods of preprocessing of the input image like edge detection, intensity mapping and HSV space conversion are done and the differences between the real and fake notes were analyzed, which can be used further for fake currency detection.ĬONTENTS o ABSTRACT o INTRODUCTION o FLOW CHART o IMAGE ACUISITION o GRAY SCALE CONVERSION o EDGE DETECTION o SOBEL OPERATORS o HISTOGRAM PLOT o MEAN CALCULATION o RESULTS AND CONCLUSION o REFERENCES ABSTRACT One of the advantages of digital image processing is the given capability of analysing an image by some distinctive features that arise from applying a filter or a transformation function.
![sketchpen for fake notes sketchpen for fake notes](https://i.pinimg.com/originals/90/02/1f/90021fe3325c0ece5272a409e2fcf256.png)
We further followed a conventional image processing-based approach and analyzed the discriminative feature differences between a real and a fake note. 10, 20, 50, 100, 500, 2000, it was found that VGG16 obtained the highest classification as well fake detection accuracy of 98.08% and 97.95%, respectively. After evaluating the proposed approach on around 2572 images belonging to 6 denominations of Rs. As we had limited datasets available for this work, the pre-trained deep learning models were expected to give better performance. The use of pre-trained deep learning models is motivated by the fact that these models are based on transfer learning and hence not data-hungry. In specific, we explored the applications of pre-trained deep learning models, like VGG16, GoogLeNet and MobileNet for currency classification and fake currency detection.
![sketchpen for fake notes sketchpen for fake notes](http://smoothrise.com/wp-content/uploads/2017/07/19407.jpg)
We have employed various deep learning architectures for this task. To address both of these problems, in this work we propose a deep learning model, which not only recognizes the real and fake Indian currency notes but also tells its correct denomination.
![sketchpen for fake notes sketchpen for fake notes](https://clipground.com/images/cute-notebook-paper-clipart-10.jpg)
Further, the other side of the issue is the problem of visually impaired people to recognize bank notes. These days, there is rampant usage of fake currency notes in every country, which is not good for the economy of that country. The result will predict whether the currency note is fake or not. The proposed system has got advantages like simplicity and high performance speed.
#Sketchpen for fake notes software
MATLAB software is used to extract the features of the note. This article describes extraction of various features of Indian currency notes.
#Sketchpen for fake notes verification
Verification of currency note is done by the concepts of image processing. The proposed system gives an approach to verify the Indian currency notes. This leads to design of a system that detects the fake currency note in a less time and in a more efficient manner. And counterfeit of currency notes is also a big problem to it. India has been unfortunately cursed with the problems like corruption and black money. As a result the issue of fake notes instead of the genuine ones has been increased very largely. Few years back, the printing could be done in a print house, but now anyone can print a currency note with maximum accuracy using a simple laser printer. The advancement of color printing technology has increased the rate of fake currency note printing and duplicating the notes on a very large scale.