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.
Abstract (translated)
手语作为听力和言语障碍人士的重要沟通方式，发挥着不可或缺的作用。然而，对于如尼泊尔手语（NSL）等代表性不足的手语来说，数字语言数据集资源仍然非常稀缺。本研究首次介绍了针对NSL的基准数据集，该数据集包含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和视觉特征。 为了评估识别性能，我们在MobileNetV2和ResNet50架构上对数据集进行了微调，分别实现了90.45%和88.78%的分类准确率。这些发现证明了卷积神经网络在手语识别任务中的有效性，尤其是在资源匮乏的情况下。 据我们所知，这项工作代表了首次系统性地为NSL构建基准数据集并评估深度学习方法的研究尝试，突显了迁移学习和微调技术对于探索未被充分研究的手语领域的潜力。
URL
https://arxiv.org/abs/2510.11243
