# Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer Learning

arXiv: https://arxiv.org/abs/2010.07827 · HTML: https://mlsanity.com/paper/2010.07827/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Word separation in continuous sign language using isolated signs and post-processing](https://mlsanity.com/paper/2204.00923/) (2022)
- [A Transformer Model for Boundary Detection in Continuous Sign Language](https://mlsanity.com/paper/2402.14720/) (2024)
- [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)
- [BDSLI: A hybrid CNN-Transformer model for Bengali Sign Language interpretation](https://mlsanity.com/paper/2609.26088/) (2026)

## Abstract (arXiv)

The automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning in order to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consist of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9,400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.