The paper introduces Maswao, a new dataset for South African Sign Language (SASL) created through automated collection and preparation of video content from public sources. The authors convert accompanying audio to text, crop sign regions, and use gradient descent to segment sentences, producing weakly sentence-aligned parallel corpora. They benchmark this dataset on sign-to-text translation, showing Transformer models outperform recurrent baselines with BLEU-1 to BLEU-4 scores up to 13.78, 5.21, 2.52, and 1.54, respectively.
By Thapelo Sindane, Unarine Netshifhefhe, Vukosi Marivate, Abiodun Modupe, Herkulaas Combrink, Ketro Sithole
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
By Lucia Yen Wanchi, Samuel Johnny, Victor Tolulope Olufemi, Emmanuel Aaron, Moise Busogi
arXiv:2606. 11925v1 Announce Type: cross Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non-signing communities.
By Zsolt Robotka, \'Ad\'am R\'ak, Jalal Al-Afandi, Andr\'as Horv\'ath, Gy\"orgy Cserey
arXiv:2605. 01720v3 Announce Type: replace-cross Abstract: Existing large-scale sign language resources typically provide supervision only at the level of raw video-text alignment and are often produced in laboratory settings.
By Sen Fang, Hongbin Zhong, Yanxin Zhang, Dimitris N. Metaxas
arXiv:2608. 09006v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks.
By Shiwei Gan, Xiao Liu, Yafeng Yin, Zhiwei Jiang, Bowen Guo, Lie Xie, Sanglu Lu, Hongkai Wen
SignSeek is a new method for learning transferable sign representations that enables efficient retrieval of signs from dictionaries using only a query video. It employs contrastive learning with saliency‑guided articulator masking, aligning same‑gloss signs across signers while focusing on the single most critical articulator per sign. Trained on 266K samples from multiple sign languages, SignSeek achieves state‑of‑the‑art cross‑corpus retrieval performance and zero‑shot generalisation to unseen British Sign Language, also improving isolated sign recognition and subtitle alignment.
By Sobhan Asasi, Ozge Mercanoglu Sincan, Richard Bowden
arXiv:2608. 09045v1 Announce Type: cross Abstract: Recent advances in sign language (SL) research have shown a trend toward unifying multiple sign language understanding (SLU) subtasks, such as isolated sign language recognition (ISLR), continuous sign language recognition (CSLR), and sign language translation (SLT), within a single framework, leading to substantial progress.
By Xiao Liu, Shiwei Gan, Yafeng Yin, Jiaxin Yin, Bowen Guo, Yaqi Sun, Zhiwei Jiang, Lei Xie
RAIDAL is an active learning framework for continuous sign language recognition that leverages the CTC decoder’s alignment peaks to focus sample selection on gloss‑aligned regions, thereby avoiding temporal redundancy in weakly aligned videos. By restricting representation‑based scoring to these decoder‑aligned gloss areas, RAIDAL improves data efficiency across multiple datasets and architectures, especially in large‑vocabulary, budget‑limited scenarios. The method requires no extra labeling cost and its implementation is publicly available on GitHub.
By Rafael A. Diniz Augusto, Gabriel L. Oliveira, Erickson R. Nascimento
SMART is a new framework that jointly tackles continuous sign language recognition (CSLR) and spotting by leveraging a multimodal large language model (MLLM) to generate motion descriptions as auxiliary semantic cues. It performs stable video‑text alignment with small batch sizes and introduces a Multi‑Scale Temporal Adapter to capture temporal interactions during transformer encoding. The framework also incorporates CSFormer, a CSLR‑guided spotting module that injects recognition‑derived gloss evidence into a boundary‑aware spotting network, enabling mutual benefit between recognition and spotting tasks.
By Eunjee Choi, JungHoon Sung, Seongwhan Cho, Chu Xin, Younggeun Choi
arXiv:2607. 03657v1 Announce Type: cross Abstract: Gloss-free Sign Language Translation (SLT) translates sign language videos into spoken-language sentences without gloss annotations, avoiding costly labeling but requiring fine-grained modeling of hands, body, and facial cues.
By Ahmed Abul Hasanaath, Bicheng Xu, Mir Rayat Imtiaz Hossain, Leonid Sigal, Hamzah Luqman
Isharah‑Selfie is a large‑scale Arabic Sign Language dataset collected entirely with front‑facing smartphone cameras in unconstrained selfie conditions. It contains 8,997 video clips from 16 signers across 1,493 unique sentences, annotated with gloss sequences for continuous sign language recognition and Arabic sentence translations for sign language translation. The dataset focuses on realistic mobile communication, featuring one‑handed signing, close‑range viewpoints, partial body visibility, camera motion, unconstrained environments, and heterogeneous device resolutions, and introduces signer‑independent and unseen‑sentence evaluation protocols.
By Ahmed Abul Hasanaath, Murtadha Aljubran, Sarah Alyami, Muhammad Haris Khan, Hamzah Luqman
arXiv:2605. 31393v2 Announce Type: replace-cross Abstract: Sign language translation (SLT) remains constrained by the limited availability of paired sign-video/text corpora and by the heavy-tailed vocabularies typical of real-world datasets.
By Pedro Dal Bianco, Jean Paul Nunes Reinhold, Oscar Stanchi, Facundo Quiroga, Franco Ronchetti, Ulisses Brisolara Corr\^ea