An Evaluation of OpenCV’s Investigation into Hand Gesture Recognition Methods - Kumar Bibhuti B. Singh; Dr. Nikhat Akhtar; Prof. (Dr.) Devendra Agarwal; Susheel Kumar; Dr. Yusuf Perwej (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
In order to achieve human-computer interaction (HCI) that is quick, accurate, and user-friendly, processing and intelligence are absolutely necessary. Despite the fact that computers are now capable of comprehending signs and symbols, the notion of identifying symbols that are produced live by a person in front of a camera is still somewhat foreign. The purpose of this work is to examine several methods for hand gesture detection by using the capabilities of OpenCV and TensorFlow, which are two of the most popular libraries in the fields of computer vision and deep learning. In order to perform preprocessing, feature extraction, and the establishment of a strong basis for subsequent research, OpenCV’s extensive image processing capabilities are employed. For the purpose of constructing and training deep neural networks that are able to recognize fine-grained features and minor changes in hand motions, TensorFlow is used. Through this integration, it is possible to differentiate and comprehend a predetermined set of motions in a precise and accurate manner, hence revealing the potential for robust hand gesture recognition systems.