Abstract:
Digital image processing plays a crucial role in enabling efficient and precise analysis, manipulation, and enhancement of images. In this study, researchers address challenges faced by individuals with visual impairments in recognizing currency denominations and identifying counterfeit banknotes. The researchers propose "Blind Trust," an IoT device that utilizes an Arduino Uno and a camera module to capture images of banknotes. To achieve these objectives, researchers utilize pre-processing techniques using the OpenCV and TensorFlow libraries to extract the notes/coins' characteristics. Custom datasets are developed for training Convolutional Neural Network (CNN) models, which are then used to identify currency denominations and detect counterfeit currency. To enhance the model's performance, various preprocessing techniques are employed, resulting in high accuracy rates for both tasks. The currency notes identification model achieves an impressive 99% accuracy when tested on 25% of the data, while the currency coins identification model achieves 93% accuracy using InceptionV3. Additionally, the counterfeit currency detection model, created using VGG16, achieves an accuracy rate of 97% on a dataset comprising genuine and counterfeit currency images. Moreover, the note placement detection model attains 93% accuracy. "Blind Trust" holds great potential for enhancing financial security and accessibility for individuals with visual impairments. Its accuracy, speed, and ease of use contribute significantly to the development of new technologies aimed at improving their quality of life.
