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arXiv Computation and Language By Adeleke Adeniyan, Abimbola H. Afolayan, Olanrewaju V. Johnson

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

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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