Hand Gesture Recognition Systems: A Review of Vision-based and Sensor-based Approaches - Akash Rahate; Akash Peddewad; Vaibhav Chamvad; Shruti Raykhelkar; A.M. Chadchankar (International Journal of Optical Innovations & Research, 2024)
With many real-world uses, such as sign language translation and human-computer interaction, hand gesture detection is a crucial area of study in the science of computer vision. In this study, we propose a Convolutional Neural Network (CNN) model that uses real-time camera images to recognise hand gestures. A collection of hand motion photographs spanning the English alphabet (A-Z) was gathered, and the images were pre-processed to exclude any backdrop and create black and white versions. The CNN model, which was trained using the pre-processed images, produced a 95% accuracy rate on the test dataset. We used Python to develop the model and combined it with an intuitive software interface to predict hand gestures in real time with the system camera. We used Python to develop the model and combined it with an intuitive software interface to predict hand gestures in real time with the system camera. Our system has the potential to be an important resource in several sectors and domains, such as helping those with speech impairments and controlling technological devices. Real-time hand gesture recognition is made reliable and accurate by the suggested model and technology, which may be applied in a variety of contexts.
Keywords: Computer vision; Convolutional neural network; Hand-sign; image preprocessing