# A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition

arXiv: https://arxiv.org/abs/2007.12530 · HTML: https://mlsanity.com/paper/2007.12530/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks](https://mlsanity.com/paper/2103.06982/) (2021)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision](https://mlsanity.com/paper/2405.10266/) (2024)
- [ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research](https://mlsanity.com/paper/2105.05066/) (2021)
- [NLP for The Greek Language: A Longer Survey](https://mlsanity.com/paper/2408.10962/) (2024)
- [A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset](https://mlsanity.com/paper/2310.04753/) (2023)
- [LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation](https://mlsanity.com/paper/2211.15481/) (2022)
- [Toward American Sign Language Processing in the Real World: Data, Tasks, and Methods](https://mlsanity.com/paper/2308.12419/) (2023)
- [SignAttention: On the Interpretability of Transformer Models for Sign Language Translation](https://mlsanity.com/paper/2410.14506/) (2024)
- [Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models](https://mlsanity.com/paper/2605.31393/) (2026)

## Abstract (arXiv)

In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a plethora of pretraining schemes is thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where sentence and gloss level annotations are provided for a video capture.