[Submitted on 3 Oct 2026]
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 exceed 1 GB to 4 GB of RAM. This study addresses constraints through a lightweight YOLO11n object-detection framework. The system provides real-time Nigerian Sign Language recognition for low-resource hardware. A benchmark dataset containing 11,208 raw gesture images was established. The data spans 33 static classes collected from five signers. Partitioning isolates signers S1 through S3 for training. Signer S4 provides validation data. Signer S5 is reserved for testing. Spatial augmentations are restricted strictly to the training set. This yields 23,509 training images. The validation and test sets each maintain 1,681 images across varied environments. The framework integrates a quantization-aware compression objective. Loss functions merge with squared Frobenius-norm penalties to govern weight distortion. A Spatial-Density Occlusion Index quantifies background complexity and self-occlusion. A latency-profiling equation relates floating-point operations and memory bandwidth to chipset clock speed. Performance testing on physical Android hardware utilizes unquantized FP32 weights. Mean inference latencies span 405.5 ms to 1,759.4 ms. Measured against test data, the algorithm attains a Precision of 0.9975. Recall reaches 0.9991. Mean Average Precision is 0.9942. Outcomes support offline functionality on constrained mobile devices. Practical deployment operates through a mobile utility using CameraX. The application captures gestures in real time. Recognized signs display as text instantly.
Submission history
From: Adeleke Adeniyan [view email]
[v1] Sat, 3 Oct 2026 19:15:16 UTC (1,097 KB)
