Sign Language Analysis System for Communicating to People
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Sign Language Analysis System for Communicating to People
Asst prof. Roshan R. Kolte
Information Technology KDK College of Engineering Nagpur, India
roshan.kolte@kdkce.edu.in
Vikas M. Manjre
Information Technology KDK College of Engineering Nagpur, India
manjarevikas2@gamil.com
Amisha M. Ramteke
Information Technology KDK College of Engineering
Nagpur, India amisharamteke@gmail.com
Achal P. Nikhade
Information Technology KDK College of Engineering Nagpur, India
achalnikhade2004@gmail.com
Anushka S. Pilare
Information Technology KDK College of Engineering Nagpur, India
anushkapilare2004@gmail.com
Abstract - Sign language is one of the most reliable ways of communicating with special needs people, as it can be done anywhere. However, most people do not understand sign language. Therefore, we have devised an idea to make a desktop application that can recognize sign language and convert it to text in real time. This research uses American Sign Language (ASL) datasets and the Convolutional Neural Networks (CNN) classification system. In the classification, the hand image is first passed through a filter and after the filter is applied, the hand is passed through a classifier which predicts the class of the hand gestures. This research focuses on the accuracy of the recognition. Our Application resulted in 96,3% accuracy for the 26 letters of the alphabet.
© 2023 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license .
Peer-review under responsibility of the scientific committee of the 7th International Conference on Computer Science and Computational Intelligence 2022
Keywords: Computer Vision; Convolutional Neural Networks; American Sign Language (ASL); Sign Language Recognition
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