# Sign Language to Text Conversion in Real Time using Transfer Learning

arXiv: https://arxiv.org/abs/2211.14446 · HTML: https://mlsanity.com/paper/2211.14446/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Built on by (most cited first, 2 of 2)

- [The Influence of Iconicity in Transfer Learning for Sign Language Recognition](https://mlsanity.com/paper/2603.03316/) (2026)
- [Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment](https://mlsanity.com/paper/2608.16804/) (2026)

## Abstract (arXiv)

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.