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New YOLOv11n Architecture Enables Nigerian Sign Language Recognition on Low-Resource Mobile Devices

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a lightweight YOLOv11n architecture designed for real-time recognition of Nigerian Sign Language on low-resource mobile devices. This framework addresses the computational demands of existing models by optimizing for hardware with limited RAM, such as standard Android phones. The system was trained on a dataset of over 11,000 gesture images and demonstrated high precision and recall, enabling offline functionality and instant text display of recognized signs through a mobile application. AI

IMPACT This development could significantly improve accessibility for deaf communities in regions with limited technological infrastructure by enabling sign language recognition on common mobile devices.

RANK_REASON The item describes a new architecture and benchmark results for a specific application, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New YOLOv11n Architecture Enables Nigerian Sign Language Recognition on Low-Resource Mobile Devices

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The item describes a new architecture and benchmark results for a specific application, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Adeleke Adeniyan, Abimbola H. Afolayan, Olanrewaju V. Johnson ·

    Edge-Optimized YOLOv11n Architecture for Offline Recognition of Nigerian Sign Language Alphanumeric Gestures

    arXiv:2611.00025v1 Announce Type: cross Abstract: Deaf inclusion across sub-Saharan society faces regional hurdles, with Nigeria as the core empirical focus. Existing recognition models demand heavy computational infrastructure. However, standard mobile phones used locally rarely…

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