Abstract:
In Sri Lanka, Sri Lankan Sign Language (SLSL) is the main way of communicating among the
deaf community; however, the present recognition systems do not have the culturally adaptive
methods that come with high accuracy. This study is going to fill this gap through a comparison
of the different deep learning approaches for SLSL recognition based on hand landmark sequences.
The dataset used is the SSL400 from Kaggle (50 SLSL signs, 3,092 training samples,
546 test samples), where MediaPipe has tracked 132-dimensional hand landmarks per frame
to cover 50-frame sequences. There were four architectures created and examined: TCN-SE,
BiLSTM-Attention, 1D CNN-GRU Hybrid, and Lightweight Transformer. Out of the four,
TCN-SE was the one that got the highest accuracy of 90.48%, which was way above the accuracy
of BiLSTM-Attention (52.20%), CNN-GRU (49.45%), and Lightweight Transformer
(50.37%). The analysis of feature importance showed that the combination of multiscale dilated
convolutions and adaptive attention mechanisms truly capture both the short-term and
long-term temporal patterns in sign language movements. TCN-SE with a training time of just
30 minutes can be called a suitable candidate for practical real-time SLSL recognition systems.
The current study provides the first thorough comparative analysis of the cuttingedge architectures
solely for SLSL recognition, thus giving the researchers the practical guidance to design
the sign language recognition technologies for the hearing-impaired community that are not
only efficient but also lowcost.
