Abstract

Abstract: This work is dedicated to develop a computer vision-based approach for Indian paper currency recognition. In this approach, extract currency feature and develop a dataset which can be used for the currency recognition. Security feature of Indian currency note available on front and back side Rs.10, Rs. 20, Rs. 50, Rs. 100, Rs. 200 Rs. 2000 and Rs. 500 denominations are used in model Training. Advances in technology have replaced people in almost every field with machines. Thanks to the introduction of machines, banking automation has reduced the burden on humans. Banking automation requires more attention to declining currency handling. When the banknote is blurred or defaced, it is difficult to identify its currency value. A sophisticated design is included to increase the security of the call. This makes the call recognition task very difficult. For correct currency recognition, it is very important to choose a good function and an appropriate algorithm. One of the main problems that blind people face is the recognition of money, especially cash. In a way, the seemingly weakened people do not think about cash settlement and run into problems related to cash transactions in their daily life. It is a useful treatment for those who are externally weakened. Studies and trials were conducted according to key points, such as watermarks, images printed on money, the value of words and numbers, and the total amount of information gathering that stimulated CNN using Transfer Learning. And the second thought after designing a proper algorithm for Indian Currency Recognition, the problem is to carry the mechanism, which can be a burden or sometimes forgotten. Therefore this design help in a lot way for easier way to recognising the Currency just by not making an extra equipment, but by designing an android app, where it is not needed to carry any extra thing, as it is included in android smart phone, which is used by almost 748 million people in India.

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