Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{154626,
        author = {Sachin.V.Suryawanshi and Prajwal.P.Kasture and Suraj.S.Malge and Sushilkumar.S.Salve},
        title = {Hand Gesture Image Recognitions Using CNN Algorithm for Smart Interaction},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {721-724},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154626},
        abstract = {Sign language is one way to communicate with deaf people. Hand gestures are one of the typical methods used in sign language. One should learn sign language to interact with them. Learning usually takes place in peer groups. There are few study materials available for sign learning. Because of this, the process of learning sign language is very difficult task. This project presents a solution that will not only automatically recognize hand gestures but also convert it into text output so that the impaired person can easily communicate with normal people. In this paper, we propose a convolution neural network (CNN) method to recognize hand gestures. To achieve the desired level of performance, The skin model and the calibration of hand position and orientation are applied to obtain the training and testing data for the CNN. Since the light condition seriously affects the skin color, we adopt a Gaussian Mixture model (GMM) to train the skin model, which is used to robustly filter out non-skin colors in an image. Then the calibrated images are used to train the CNN.},
        keywords = {convolution neural network (CNN), Hand Gesture, MobileNet, Sign. },
        month = {},
        }