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Dr. K. Smitha, S. Sathwika, V. Harshini, P. Jyothi


Currency is an unavoidable part of our day-today life. Despite the rapidly expanding utilization of master cards and additional electronic payment categories, money is considerably utilized for everyday exchanges because of its comfort. The current day monetary self-service gives birth to currency recognition, which plays a vital role in the automated banking procedure. Therefore, we propose a novel method for currency recognition that identifies Indian currency in different views on the scale. It is straightforward for a typical human being to comprehend and recognize any banknote easily, but it is undoubtedly troublesome for anyone with a visually impaired or blind individual to accomplish a similar task. Banknotes commonly have unique designs according to the denomination and can be sorted with surplus human errors in the bank. These errors lead to difficulties in evaluating and recognition. If computers or mobile apps recognize currency, it will immensely boost the precision of recognition and ameliorate people's workload efficiently. As money has a significant role in daily life for any business transactions, real-time detection and recognition of banknotes become necessary for a person, especially blind or visually impaired, or for a system that sorts the data.

This work presents an Indian Currency Prediction Analysis, proposes an optimized model to recognize the currencies effectively. The Deep Learning approach of convolutional neural network model technique has improved the effective analysis of currency recognition with improved accuracy, high speed and efficiency along with complete automatic readily procedure with no human intervention and minimal complexity. The model which we worked on essentially classifies the currency note into distinct denominations like Rs10, Rs50, Rs100, Rs500, Rs2000. The currency will be recognized and classified by using image processing techniques, deep learning techniques

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