CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING

Computer Engineering

Dynamic head and hand movements, as well as their continually shifting shapes, are regarded a difficult topic in computer vision. Hand segmentation, hand form feature illustration, and gesture sequences recognition are the three key issues in developing an effective sign language system that can distinguish dynamic standalone motions. These methods use colour scheme hand different algorithms to divide hands into segments, hand-crafted features for hand form representations, and Hidden Markov Model (HMM) sequence recognition for traditional sign language identification. a Convolutional neural network (CNN) can be used to recognise Indian sign language motions, according to this article (CNN). It's never easy to have a meaningful conversation with someone who has hearing loss. People with speech and hearing disabilities can use sign language to communicate their thoughts and feelings to the world, making it the ultimate remedy. It facilitates and simplifies the process of integrating them with the rest of society. It is not enough, however, that sign language has been invented. As a result, there are a lot of strings connected. For people who have never learned sign language or who are fluent in a different language, the sign movements can be difficult to decipher. Various strategies for automating the identification of sign motions have made it possible to close the long-standing communication gap.

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