Open AccessSign Language Recognition and Converting into Text
Author(s) -
Shaheen Tabassum,
R Raghavendra
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41266
Subject(s) - gesture , sign language , computer science , convolutional neural network , american sign language , variety (cybernetics) , gesture recognition , sign (mathematics) , meaning (existential) , feature (linguistics) , representation (politics) , set (abstract data type) , artificial intelligence , speech recognition , natural language processing , linguistics , psychology , mathematical analysis , philosophy , mathematics , politics , political science , law , psychotherapist , programming language
Sign language is a mode of communication that use a variety of hand movements and actions to convey a message. Deciphering these motions might be a pattern recognition challenge. People use a range of gestures and behaviours to communicate with one another. This study is a system for a human-computer interface that can identify american sign language gestures and produce textual output that reflects the meaning of the gesture. To identify and learn gestures, the proposed system would employ convolutional neural networks and long short term memory networks. This will help to break down the communication gap. Keywords: Data Set, Feature Extraction and Representation, Artificial Neural Networks, Convolutional Neural Networks, Tensor Flow, Keras, OpenCV.