2025/11/17 by Alishzade, Nigar, Abdullayeva, Gulchin
Computer Science · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Hearing Impairment and Communication #I.2.10 #Interactive and Immersive Displays
paper · doi:10.48550/arxiv.2511.13126
openalex publication_date 2025/11/17 · openalex created_date 2025/11/19 · openalex updated_date 2026/07/28
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.