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Znaki: A Russian Fingerspelling Recognition Dataset — arXiv:2611.03136

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03136
License (as archived): CC0-1.0
Archived: 2026-11-03T18:55:21.045Z
SHA-256 of the archived text: 05ec30c8ff788746577b6ea2979a9ee76e0c66c2b3da9c4fd5e6d8f722710407
Stable URL of this page: https://metraipolis.com/documento/dc_e57697bb5efe

How to cite

Znaki: A Russian Fingerspelling Recognition Dataset — arXiv:2611.03136 — arXiv (en), https://arxiv.org/abs/2611.03136 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e57697bb5efe (Metraipolis archive dc_e57697bb5efe, sha256 05ec30c8ff788746577b6ea2979a9ee76e0c66c2b3da9c4fd5e6d8f722710407)

BibTeX

@misc{dce57697bb5efe,
  title = {Znaki: A Russian Fingerspelling Recognition Dataset — arXiv:2611.03136},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03136},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e57697bb5efe; sha256 05ec30c8ff788746577b6ea2979a9ee76e0c66c2b3da9c4fd5e6d8f722710407},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03136. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Fingerspelling - the sequential signing of letters that compose proper names, technical terms, and out-of-vocabulary words - is an integral component of any sign language. Despite Russian Sign Language (RSL) being used by more than 150,000 native signers, no public fingerspelling dataset for RSL exists, in contrast to American, British, German, and several other sign languages. We introduce Znaki the first open RSL fingerspelling dataset: 37,252 video recordings (35.4 hours) of 1,593 unique sequences - proper names sampled from 16 domain categories - recorded by 68 native RSL signers (37 Deaf, 29 hard-of-hearing, and 2 certified interpreters) through a crowdsourcing pipeline with a two-stage signer qualification procedure and expert validation. The dataset covers all 33 Cyrillic handshapes, and its letter-frequency distribution matches the Russian National Corpus. As reference points for future research, we establish two baselines adapted from American Sign Language fingerspelling recognition: a Squeezeformer-based encoder-decoder, reaching a character-level accuracy ($1-\mathrm{CER}$) of 0.932 and a sequence accuracy of 0.596, and Fingerspelling-PoseNet, reaching 0.886 and 0.545, respectively.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.03136 — 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 2026-11-03T18:16:53.053Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).