Currency Recognisation Using Hybrid Mode

Abstract

Currency recognition plays a crucial role in various automated systems, such as vending machines, ATMs, and cash counting devices. This paper proposes a hybrid approach to currency recognition by integrating both software and hardware components. The hybrid model combines the strengths of computer vision algorithms in software and dedicated hardware modules to enhance speed and accuracy in currency identification.The software component employs advanced image processing techniques, including feature extraction and pattern recognition, to analyze currency notes. Additionally, a machine learning model is integrated for improved adaptability to varying currency designs and conditions. On the hardware side, a dedicated microcontroller or FPGA-based system is employed to accelerate the processing of image data, reducing latency and enhancing real-time performance.The synergy between software and hardware components results in a robust and efficient currency recognition system. This hybrid model not only achieves high accuracy but also ensures rapid identification, making it suitable for applications demanding quick and reliable currency processing. The proposed approach opens avenues for enhancing the automation and security of financial systems by providing an integrated solution for currency recognition.