The Finger Name Detection System leverages the computational power of the Raspberry Pi to identify and label different types of human fingers based on real-time camera input. This innovative system employs computer vision techniques to analyze live video feed from a connected camera and accurately classify various finger types—such as thumb, index, middle, ring, and pinky.
Utilizing the Raspberry Pi’s processing capabilities and camera module, the system captures high-resolution images of the user’s hand and applies pre-trained machine learning models to detect and classify each finger. The detected finger type is then displayed on a screen or interface, providing immediate and clear identification.
This project demonstrates a practical application of Raspberry Pi in the field of image recognition and computer vision, combining hardware and software to create an interactive and informative system. It has potential applications in various fields, including educational tools, accessibility devices, and interactive user interfaces. The system’s modular design allows for future enhancements, such as incorporating gesture recognition or extending the functionality to identify specific hand gestures.
Project Code:
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