CLASSIFICATION MODEL FOR INDIAN CURRENCY USING DEEP LEARNING

The field of deep learning has revolutionized computer vision and image recognition, enabling significant advancements in various domains. Image recognition, in particular, has become a prominent use case for deep learning, finding applications in diverse fields for tasks such as image filtering and categorization. One domain where image processing plays a crucial role is the banking sector, where it is used to classify and verify currency notes. In this project, our aim is to propose a machine learning model specifically designed for the classification of Indian currency notes and coins. The model will have the capability to accurately classify and identify the denomination value of paper notes as well as coins. This classification system can be instrumental in detecting fake and counterfeit currencies, providing a valuable tool for ensuring the integrity of financial transactions. Furthermore, the proposed model can be integrated into existing notes and coins counters, as well as vending machines, to automate the classification process and enhance efficiency. By leveraging machine learning techniques, this project aims to streamline currency recognition processes and provide a reliable solution for accurately identifying the denomination value of Indian currency, benefiting various industries and sectors that deal with cash transactions. Keywords—Deep Learning, Convolutional Neural Network, Image Classification, Feature Extraction, Currency Recognition, Data Augmentation