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.

翻译：手语是听力和言语障碍人士的重要交流系统。然而，对于尼泊尔手语等代表性不足的手语，其数字语言数据集资源仍然匮乏。本研究首次引入了针对尼泊尔手语的基准数据集，包含36个手势类别，每个类别有1500个样本，旨在捕捉该语言的结构与视觉特征。为评估识别性能，我们在该数据集上对MobileNetV2和ResNet50架构进行了微调，分别实现了90.45%和88.78%的分类准确率。这些结果表明了卷积神经网络在手语识别任务中的有效性，尤其在低资源环境下。据我们所知，本研究是首次系统性地构建基准数据集并评估尼泊尔手语识别深度学习方法的工作，凸显了迁移学习和微调技术在推动未充分探索手语研究方面的潜力。