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Maswao: An automatically collected, prepared, and benchmarked dataset for the South African Sign language — arXiv:2611.02903

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02903
License (as archived): CC0-1.0
Archived: 2026-11-03T10:58:46.680Z
SHA-256 of the archived text: 25eff61960536d2b26a7b17a39a9cf4dfa2ee1b2341892872b8c4a732bb877a4
Stable URL of this page: https://metraipolis.com/documento/dc_f7bdfd806353

How to cite

Maswao: An automatically collected, prepared, and benchmarked dataset for the South African Sign language — arXiv:2611.02903 — arXiv (en), https://arxiv.org/abs/2611.02903 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f7bdfd806353 (Metraipolis archive dc_f7bdfd806353, sha256 25eff61960536d2b26a7b17a39a9cf4dfa2ee1b2341892872b8c4a732bb877a4)

BibTeX

@misc{dcf7bdfd806353,
  title = {Maswao: An automatically collected, prepared, and benchmarked dataset for the South African Sign language — arXiv:2611.02903},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02903},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f7bdfd806353; sha256 25eff61960536d2b26a7b17a39a9cf4dfa2ee1b2341892872b8c4a732bb877a4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02903. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent advancements in sign language processing can be attributed to developments in both large-scale dataset collection and algorithmic complexity in areas such as pose estimation techniques, signal processing through Transformer models, and model optimization. However, only a handful of sign languages, such as the American, British, and German, from over 200 globally recognized sign languages, reap the benefits of such advancements, while the rest lag behind. In this study, we aim to address this technological disparity by proposing a framework for the automatic collection and preparation of sign language datasets, with very minimal human-labour intervention, using the South African Sign Language (SASL) as a testbed. Specifically, we automatically collect SASL video content using public resources such as news broadcasts. Additionally, we convert their accompanying audio streams into text using available Automatic Speech Recognition (ASR) tools. Furthermore, we crop the sign language area in the videos and use gradient descent to locate valleys of non-motion to determine end-of-sentence points within long videos to create our weakly sentence-aligned parallel corpora -- the Maswao (Signs) dataset. Finally, we benchmark the weakly aligned dataset on the task of Sign-to-text Translation using Recurrent Neural Networks and Transformer models to compare their individual intricacies. Our results show that Transformer models proposed in the literature outperform recurrent baselines, with the best-performing Transformer achieving BLEU-1 to BLEU-4 scores of 13.78, 5.21, 2.52, and 1.54, respectively, as well as ChrF = 18.09 and ROUGE-L = 10.51. These results indicate that the task remains promising and warrants further investigations.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02903 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:49:38.694Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).