Abstract: Sign language (SL) is a visual language that people with speech and hearing disabilities use to communicate in their everyday conversations. It is entirely an optical communication language due to its native grammar. Sadly, learning and practicing sign language is not that common in our society; as a result, this research created a prototype for sign language recognition. Hand detection was used to create a system that will serve as a learning tool for sign language beginners. We have created an improved Deep CNN model that can recognize which letter, word, or digit of the American Sign Language (ASL) is being signed from an image of a signing hand. We have extracted the features from the images by using Transfer Learning and building a model using a Deep Convolutional Neural Network or Deep CNN. We have evaluated the proposed model with our custom dataset as well as with an existing dataset. Our improved Deep CNN model gives only a 4.95% error rate. In addition, we have compared the improved Deep CNN model with other traditional methods and here the improved Deep CNN model achieved an accuracy rate of 95% and outperforms the other models.
Keywords: Transfer Learning, Reinforcement Learning, Sign Language, Deep Convolutional Neural Networks, VGG16.
Title: Isolated sign language recognition using hidden transfer learning
Author: Kausar Mia, Tariqul Islam, Md Assaduzzaman, Sonjoy Prosad Shaha, Arnab Saha, Md. Abdur Razzak, Angkur Dhar, Teerthanker Sarker, Al Imran Alvy
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 11, Issue 1, January 2023 - March 2023
Page No: 28-38
Research Publish Journals
Website: www.researchpublish.com
Published Date: 07-February-2023
