@inproceedings{r-etal-2025-continuous,
    title = "Continuous Fingerspelling Dataset for {I}ndian {S}ign {L}anguage",
    author = "R, Kirandevraj  and
      Kurmi, Vinod K.  and
      Namboodiri, Vinay P.  and
      Jawahar, C.v.",
    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.6/",
    pages = "33--38",
    ISBN = "979-8-89176-304-3",
    abstract = "Fingerspelling enables signers to represent proper nouns and technical terms letter-by-letter using manual alphabets, yet remains severely under-resourced for Indian Sign Language (ISL). We present the first continuous fingerspelling dataset for ISL, extracted from the ISH News YouTube channel, in which fingerspelling is accompanied by synchronized on-screen text cues. The dataset comprises 1,308 segments from 499 videos, totaling 70.85 minutes and 14,814 characters, with aligned video-text pairs capturing authentic coarticulation patterns. We validated the dataset quality through annotation using a proficient ISL interpreter, achieving a 90.67{\%} exact match rate for 150 samples. We further established baseline recognition benchmarks using a ByT5-small encoder-decoder model, which attains 82.91{\%} Character Error Rate after fine-tuning. This resource supports multiple downstream tasks, including fingerspelling transcription, temporal localization, and sign generation. The dataset is available at the following link: https://kirandevraj.github.io/ISL-Fingerspelling/."
}