Deep Learning-Based Currency Recognition: A Money Identifier System

The paper presents a comprehensive study on the development of a currency recognition system using deep learning techniques, specifically focusing on convolutional neural networks (CNNs). The primary objective is to create an accurate and efficient money identifier capable of recognizing various banknotes in real-world applications, such as automated payment systems and anti-counterfeiting measures. To achieve this, we constructed a diverse dataset consisting of over 10,000 images of banknotes from multiple currencies, ensuring representation across different denominations and capturing images under varying lighting conditions and angles. This diversity is crucial for training a model that is robust to real-world scenarios where variations in lighting, orientation, and occlusion may occur. The data preprocessing phase included normalization and augmentation techniques, such as random rotations, zooming, and flipping, to enhance the training process and improve the model's ability to generalize to unseen data. We implemented a CNN architecture featuring multiple convolutional layers followed by max-pooling layers, a fully connected layer, and an output layer with a softmax activation function for multi-class classification.