Sign Language Alphabet Detection System

Sanya Srivastava; Shubham Yadav; Upvan Tripathi; Saksham Pathak; Sameer Awasthi

Abstract

Currently, gesture recognition for hands and sign language detection for human-computer interaction isthe main emphasis of computer vision and machine learning. The creation of systems that can identifygestures and use them to transmit data or control devices is one of the main goals. Specifically, handgestures entail dynamic hand movements that require explanations in both the spatial and temporaldomains, whereas hand postures reflect the static structure of the hand. The goal of this project is todevelop a vision-based system that can recognise sign language in real time. A vision-based systemmakes sense because it allows people to engage with computers in a more direct and organic way. Theinvestigation of interaction between people and machines through recognition of gestures hasled to the integration of this type of technology throughout multiple fields, given the importanceof the hand of humans as a means of communication in daily life and the ongoing advancementof the processing of video and images methods. Virtual reality, interactive screens, video gameconsoles, medical apps, and sign language recognition are a few of these. While sign languageis a prevalent means of communication for deaf people, there are obstacles when deaf peopleinteract with hearing people in traditional contexts. The goal of this research is to help addressthese issues and promote more seamless interactions between hearing and deaf people.

Sign Language Alphabet Detection System

Abstract

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Abstract

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