Authors - Chandan Raj B R, A. Yasaswi, Deepika K, Uday Bhaskar Reddy, Delina Yadav K, Joshna K Abstract - It is quite difficult to communicate with deaf individuals. This article extends the complexity of Indian Sign Language (ISL) character classification. Sign language is insufficient for the hearing and speaking disabled. Hand gestures of disabled individuals may appear confused to those who have not learnt the language. Communication should be two-way. In this essay, we will discuss how to learn a language through sign language. Images are processed using computer vision processes, including grayscale conversion, dilation, and masking. We employ Convolutional Neural Networks (CNN) to train and recognize images. Our example has an accuracy of approximately 95%. Gestures serve as a nonverbal communication tool in language. People with hearing or speech difficulties frequently utilize them to communicate with others or among themselves. Many loudspeakers are created by various manufacturers around the world. This study demonstrates that many experiments are undertaken each year, with several articles published in journals and conferences, and that research on vision-based gesture recognition is ongoing. Cognitive navigation focuses on three areas: information retrieval, environmental information, and gesture representation. In terms of identity verification, we also evaluated the authentication system's effectiveness. The physical movement of the human hand generates gestures, and gesture recognition contributes to improvements in autonomous vehicle operation. This paper use the convolutional neural network (CNN) classification technique to detect and recognize human motions. This workflow consists of region-of-interest coordination via masking, finger segmentation, normalization of segmented finger pictures, and finger recognition using a CNN classifier. Use the mask to separate the hand portion of the image from the rest of the image. The histogram equalization approach is used to improve the contrast of each pixel in an image. This work uses a variety of scanning techniques to classify fingerprints from hand photographs. The segmented fingers from the hand image are put into the CNN classification algorithm, which separates the image into different groups. This research proposes gesture recognition and recognition methods based on CNN classification, and the technology achieves good performance using cutting-edge methodologies.
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