arXiv AI
Aug 10
TransSLR: A Lightweight Transformer for Sign Language Recognition
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
BDSLI: A hybrid CNN-Transformer model for Bengali Sign Language interpretation
The paper presents BDSLI, a hybrid CNN‑Transformer architecture designed for Bengali Sign Language Recognition (SLR). It introduces a new model combination for Bengali SLR, trained on a custom video dataset of 62 sign words (250 samples per class) and evaluated against baseline models. The hybrid model achieved high accuracy—99.58% training, 99.48% validation, and 98.65% test—before being deployed in a web application for real‑world testing.
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arXiv AI
Jul 7
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition
arXiv:2204. 02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D observations and because the signal is inherently spatiotemporal.
arXiv AI
Aug 7
Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model
arXiv:2608. 06252v1 Announce Type: cross Abstract: Deaf and hard-of-hearing people in Bangladesh communicate mainly through Bangla Sign Language (BdSL).
arXiv Computer Vision
Sep 11
Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation
The paper introduces BdSLIG, the first Bengali Sign Language Instruction Generation dataset, aimed at evaluating Vision Language Models on under-resourced SLIG tasks and long-tail visual concepts. It proposes Sign Parameter-Infused (SPI) prompting, which embeds standard sign parameters such as hand shape, motion, and orientation into textual prompts to improve zero-shot performance and produce more structured, reproducible instructions. The work seeks to promote inclusivity and advance sign language learning systems for under-resourced communities.
arXiv Computer Vision
Oct 2
Open Vocabulary Word Recognition From Transcribed Bangla Texts
arXiv:2610.01134v1 Announce Type: new Abstract: An optical character recognition (OCR) can scan a paper and extract text using technology, making people's jobs easier. While various OCR systems are a...
arXiv AI
Oct 15
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition
arXiv:2610.14680v1 Announce Type: new Abstract: Sign Language Recognition (SLR) presents complex challenges due to variable signing speeds and spatial intricacies. Specifically, Indian Sign Language...
arXiv Computation and Language
Sep 14
Investigating Temporal Motion Features for Pose-to-Text Indian Sign Language Translation
arXiv:2609.12993v1 Announce Type: cross Abstract: We investigate the effect of pretrained T5 model scale and explicit motion features on pose-to-text Indian Sign Language Translation (SLT) for the WS...
arXiv Computer Vision
Aug 27
SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting
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.
arXiv AI
Jul 14
Emotion Recognition in Signers
arXiv:2512. 15376v2 Announce Type: replace-cross Abstract: Recognition of signers' emotions suffers from one theoretical challenge and one practical challenge, namely, the overlap between grammatical and affective facial expressions and the scarcity of data for model training.
arXiv AI
Jul 7
ViPo-MLLM: Visual-Pose Multimodal LLM for Gloss-Free Sign Language Translation
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.
arXiv Machine Learning
Jun 11
Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching
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.
arXiv AI
Jul 21
SyriSign: A Parallel Corpus for Arabic Text to Syrian Arabic Sign Language Translation
arXiv:2603. 29219v2 Announce Type: replace-cross Abstract: Sign language is the primary approach of communication for the Deaf and Hard-of-Hearing (DHH) community.