@inproceedings{reddy-kamakshi-2025-pose,
    title = "Pose-Based Temporal Convolutional Networks for Isolated {I}ndian {S}ign {L}anguage Word Recognition",
    author = "Reddy, Tatigunta Bhavi Teja  and
      Kamakshi, Vidhya",
    editor = "Hasanuzzaman, Mohammed  and
      Quiroga, Facundo Manuel  and
      Modi, Ashutosh  and
      Kamila, Sabyasachi  and
      Artiaga, Keren  and
      Joshi, Abhinav  and
      Singh, Sanjeet",
    booktitle = "Proceedings of the Workshop on Sign Language Processing (WSLP)",
    month = dec,
    year = "2025",
    address = "IIT Bombay, Mumbai, India (Co-located with IJCNLP{--}AACL 2025)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wslp-main.8/",
    pages = "47--50",
    ISBN = "979-8-89176-304-3",
    abstract = "This 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."
}