The paper presents a lightweight YOLOv11n-based framework for real‑time recognition of Nigerian Sign Language alphanumeric gestures on low‑resource mobile devices. It introduces a benchmark dataset of 11,208 images across 33 static classes, with a training set of 23,509 images and separate validation and test sets of 1,681 images each. The model, optimized with quantization‑aware compression and a spatial‑density occlusion index, achieves high precision (0.9975) and recall (0.9991) with inference latencies between 405.5 ms and 1,759.4 ms on Android hardware.
arXiv:2608. 06252v1 Announce Type: cross Abstract: Deaf and hard-of-hearing people in Bangladesh communicate mainly through Bangla Sign Language (BdSL).
arXiv:2607. 02371v1 Announce Type: cross Abstract: Over 285 million people worldwide live with a visual impairment, for whom everyday tasks such as avoiding obstacles, locating personal belongings, recognizing familiar faces, or handling cash remain persistent obstacles to personal autonomy.
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.
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:2606. 24900v1 Announce Type: new Abstract: This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors.
arXiv:2608. 10588v1 Announce Type: cross Abstract: Purpose: Fine-grained handshape recognition supports computational sign-language transcription, recognition, and translation, but broad, phonetically defined visual inventories with signer-aware evaluation remain limited.
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
arXiv:2607. 13164v1 Announce Type: cross Abstract: Sign language is a primary communication channel for millions of Deaf and hard-of-hearing people, yet text-to-signer video generation remains costly because video diffusion models are expensive to train and evaluate.
arXiv:2608. 03444v1 Announce Type: cross Abstract: Sign language recognition (SLR) enhances communication between hearing and hearing-impaired individuals.
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
arXiv:2608. 09400v1 Announce Type: cross Abstract: Research regarding the sign language recognition mostly relies on RGB images, whileas sign language datasets that provide depth images are limited.
This paper addresses the challenges of using sign language within shared mobility services, such as taxis, carpools, or ride-sharing platforms. The use of sign language recognition (SLR) in real-world, confined environments, specifically vehicle interiors remains largely unexplored.