{"ID":2840466,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13126","arxiv_id":"2511.13126","title":"A Comparative Analysis of Recurrent and Attention Architectures for Isolated Sign Language Recognition","abstract":"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.","short_abstract":"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 Langu...","url_abs":"https://arxiv.org/abs/2511.13126","url_pdf":"https://arxiv.org/pdf/2511.13126v1","authors":"[\"Nigar Alishzade\",\"Gulchin Abdullayeva\"]","published":"2025-11-17T08:28:35Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Transformer\"]","has_code":false}