Nepali Sign Language Characters Recognition: Dataset Development and Deep Learning Approaches

arXiv:2510.11243 · cs.CV, cs.AI · Submitted 2025-10-13 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-10-13

Updated: 2025-10-13

Comments: 6 pages, 9 figures

Journal ref: KEC Journal of Science and Engineering 10 (2026) 131-136

License: http://creativecommons.org/licenses/by/4.0/

The gist: Sign languages serve as essential communication systems for individuals with hearing and speech impairments.

Terminology

Accuracy — 19× in this paper · explained in 1 episode
Nepali Sign Language — 17× in this paper
Fine-Tuning — 9× in this paper · explained in 5 episodes
Transfer Learning — 9× in this paper · explained in 10 episodes
Benchmark — 8× in this paper · explained in 2 episodes
Convolutional Neural Network — 5× in this paper
Loss Function — 4× in this paper · explained in 2 episodes
Adam — 3× in this paper · explained in 2 episodes
Feature Extraction — 2× in this paper · explained in 2 episodes
Generalization — 2× in this paper · explained in 13 episodes
Robustness — 2× in this paper · explained in 5 episodes
Computational overhead — 1× in this paper · explained in 2 episodes
Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
Core — 1× in this paper · explained in 2 episodes
Edge Devices — 1× in this paper · explained in 2 episodes
F1 Score — 1× in this paper · explained in 3 episodes
Hybrid Models — 1× in this paper · explained in 2 episodes
Redundancy — 1× in this paper · explained in 2 episodes

Abstract

Sign languages serve as essential communication systems for individuals with hearing and speech impairments. However, digital linguistic dataset resources for underrepresented sign languages, such as Nepali Sign Language (NSL), remain scarce. This study introduces the first benchmark dataset for NSL, consisting of 36 gesture classes with 1,500 samples per class, designed to capture the structural and visual features of the language. To evaluate recognition performance, we fine-tuned MobileNetV2 and ResNet50 architectures on the dataset, achieving classification accuracies of 90.45% and 88.78%, respectively. These findings demonstrate the effectiveness of convolutional neural networks in sign recognition tasks, particularly within low-resource settings. To the best of our knowledge, this work represents the first systematic effort to construct a benchmark dataset and assess deep learning approaches for NSL recognition, highlighting the potential of transfer learning and fine-tuning for advancing research in underexplored sign languages.

Terminology

Abstract

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