# A Comparative Analysis of Recurrent and Attention Architectures for Isolated Sign Language Recognition

arXiv: https://arxiv.org/abs/2511.13126 · HTML: https://mlsanity.com/paper/2511.13126/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Learning Sign Language Representation using CNN LSTM, 3DCNN, CNN RNN LSTM and CCN TD](https://mlsanity.com/paper/2412.18187/) (2024)

## Abstract (arXiv)

This study presents a systematic comparative analysis of recurrent and attention-based neural architectures for isolated sign language recognition. We implement and evaluate two representative models-ConvLSTM and Vanilla Transformer-on the Azerbaijani Sign Language Dataset (AzSLD) and the Word-Level American Sign Language (WLASL) dataset. Our results demonstrate that the attention-based Vanilla Transformer consistently outperforms the recurrent ConvLSTM in both Top-1 and Top-5 accuracy across datasets, achieving up to 76.8% Top-1 accuracy on AzSLD and 88.3% on WLASL. The ConvLSTM, while more computationally efficient, lags in recognition accuracy, particularly on smaller datasets. These findings highlight the complementary strengths of each paradigm: the Transformer excels in overall accuracy and signer independence, whereas the ConvLSTM offers advantages in computational efficiency and temporal modeling. The study provides a nuanced analysis of these trade-offs, offering guidance for architecture selection in sign language recognition systems depending on application requirements and resource constraints.