Abstract
Hand gesture recognition is an important aspect of human-computer interaction. It forms the basis of sign language for the visually impaired people. This work proposes a novel hand gesture recognizing system for the differently-abled persons. The model uses a convolutional neural network, known as VGG-16 net, for building a trained model on a widely used image dataset by employing Python and Keras libraries. Furthermore, the result is validated by the NUS dataset, consisting of 10 classes of hand gestures, fed to the model as the validation set. Afterwards, a testing dataset of 10 classes is built by employing Google's open source Application Programming Interface (API) that captures different gestures of human hand and the efficacy is then measured by carrying out experiments. The experimental results show that by combining a transfer learning mechanism together with the image data augmentation, the VGG-16 net produced around 98% accuracy.
Abstract (translated)
手部手势识别是人机交互的重要组成部分，它是视障人士使用的手语的基础。本研究提出了一种针对残障人士的手势识别系统。该模型采用卷积神经网络（称为VGG-16网络），利用Python和Keras库在广泛使用的一组图像数据上建立训练模型。此外，结果通过新加坡国立大学（NUS）数据集进行验证，该数据集中包含10类手部手势，并将其作为模型的验证集输入。随后，构建了一个包含10个类别测试数据的数据集，利用Google开源的应用编程接口（API），捕捉人类不同手势动作的有效性并执行实验以测量其效能。实验结果显示，通过结合迁移学习机制和图像数据增强技术，VGG-16网络达到了约98%的准确率。
URL
https://arxiv.org/abs/2601.08262
