Pose-Based Temporal Convolutional Networks for Isolated Indian Sign Language Word Recognition

AbstractThis paper presents a lightweight and efficient baseline for isolated Indian Sign Language (ISL) word recognition developed forthe WSLP-AACL-2025 Shared Task. Wepropose a two-stage framework combiningskeletal landmark extraction via MediaPipeHolistic with a Temporal Convolutional Network (TCN) for temporal sequence classification. The system processes pose-basedinput sequences instead of raw video, significantly reducing computation and memorycosts. Trained on the WSLP-AACL-2025dataset containing 4,398 isolated sign videosacross 4,361 word classes, our model achieves54% top-1 and 78% top-5 accuracy.