Hand gesture recognition is an important field of study for providing an alternative means of communication for individuals who are unable to speak. The Indian Sign Language (ISL) is one such language used by the deaf and mute community in India. In this paper, we propose a vision-based hand gesture recognition system for ISL using Convolutional Neural Network (CNN). The system captures hand gestures using a webcam and processes the images using a CNN trained on a dataset of ISL gestures. The system achieved a recognition accuracy of 93.5% on the test dataset, demonstrating its effectiveness in recognizing hand gestures in the ISL language. The proposed system provides a promising solution for helping the deaf and mute community in India to communicate more effectively and efficiently.To determine the shape of the sign, the first segmentation step is done based on skin color. After that, the discovered region is converted to a binary image. The binary image is then transformed using the Euclidean distance transformation. On the distance-modified picture, row and column projections are used. Central moments, as well as HU’s moments, are done to extract features. SVM and CNN are used for classification.

Boinpally Ashwanth,Sri Bhargav Ventrapragada,Shradha Reddy Prodduturi,Jeshwanth Reddy Depa,K. Venkatesh Sharma."Vision-based Hand Gesture Recognition for Indian Sign Language Using Convolution Neural Network". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.10, Issue 01,pp.1-9, February - 2023, URL :https://ijcert.org/ems/ijcert_papers/V10I0101.pdf

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