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arXiv · 2211.14446

Sign Language to Text Conversion in Real Time using Transfer Learning

Shubham Thakar·Samveg Shah·Bhavya Shah·Anant V. Nimkar
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Abstract

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.

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Shubham Thakar, Samveg Shah, Bhavya Shah, Anant V. Nimkar. 2022-11-13. Sign Language to Text Conversion in Real Time using Transfer Learning. https://arxiv.org/abs/2211.14446

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