Sign Language to Text Conversion in Real Time using Transfer Learning
This addresses communication barriers for hearing-impaired individuals by providing a real-time translation tool, though it appears incremental as it builds on existing transfer learning methods.
The paper tackles real-time sign language to text conversion by proposing a deep learning model for American Sign Language (ASL), achieving an accuracy improvement from 94% with a CNN to 98.7% using transfer learning with VGG16, a 5% gain.
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.