# Fine-tuning of sign language recognition models: a technical report

arXiv: https://arxiv.org/abs/2302.07693 · HTML: https://mlsanity.com/paper/2302.07693/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [Transferring Cross-domain Knowledge for Video Sign Language Recognition](https://mlsanity.com/paper/2003.03703/) (2020)
- [Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?](https://mlsanity.com/paper/1907.10292/) (2019)
- [Lightweight Network Architecture for Real-Time Action Recognition](https://mlsanity.com/paper/1905.08711/) (2019)

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

- [Self-Supervised Video Transformers for Isolated Sign Language Recognition](https://mlsanity.com/paper/2309.02450/) (2023)
- [Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks](https://mlsanity.com/paper/2506.04367/) (2025)

## Abstract (arXiv)

Sign Language Recognition (SLR) is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. %Skeleton Aware Multi-modal Sign Language Recognition In this work, we focused on investigating two questions: how fine-tuning on datasets from other sign languages helps improve sign recognition quality, and whether sign recognition is possible in real-time without using GPU. Three different languages datasets (American sign language WLASL, Turkish - AUTSL, Russian - RSL) have been used to validate the models. The average speed of this system has reached 3 predictions per second, which meets the requirements for the real-time scenario. This model (prototype) will benefit speech or hearing impaired people talk with other trough internet. We also investigated how the additional training of the model in another sign language affects the quality of recognition. The results show that further training of the model on the data of another sign language almost always leads to an improvement in the quality of gesture recognition. We also provide code for reproducing model training experiments, converting models to ONNX format, and inference for real-time gesture recognition.