The people in the world who are hearing impaired face many obstacles in communication and require an interpreter to comprehend what a person is saying. There has been constant scientific research and the existing models lack the ability to make accurate predictions. So we propose a deep learning model trained on ASL i.e. American Sign Language which will take actions in the form of ASL as input and translate it into text. To achieve the translation a Convolution Neural Network model and a transfer learning model based on the VGG16 architecture are used. There has been an improvement in accuracy from 94% of CNN to 98.7% of Transfer Learning, an improvement of 5%. An application with the deep learning model integrated has also been built.
@article{arxiv.2211.14446,
title = {Sign Language to Text Conversion in Real Time using Transfer Learning},
author = {Shubham Thakar and Samveg Shah and Bhavya Shah and Anant V. Nimkar},
journal= {arXiv preprint arXiv:2211.14446},
year = {2022}
}
Comments
Will be published in IEEE Explore; Typos corrected