Abstract: Sign language is indeed the best mode of communication for people who are unable to talk or pay attention to anything. Sign language allows physically challenged people who are physically challenged to express their thoughts and emotions. There are numerous methods for recognizing hand gestures, including Random K-NN, Tree K-NN, and Fuzzy K-NN. The K-Nearest Neighbor method seems to be worth investigating. While the weighting method
is used to improve classification accuracy, the Simple Multi-Attribute Rating Technique (SMART) can then be used to optimize classification accuracy results. A novel framework of signal language reputation has been presented in this project for determining the alphabets and gesticulations in signal language. We can locate the symptoms with the help of computer imaginative and prescient neural networks.
Keywords: Sign Language Recognition, Convolution Neural Network, Image Processing, Edge Detection, Hand Gesture Recognition.
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DOI:
10.17148/IJARCCE.2022.11549
