Department of Cyber Security, Air University, Islamabad, Pakistan.
Department of Computer Science, University of Lahore, Lahore, Pakistan.
Comput Intell Neurosci. 2022 Apr 30;2022:1450822. doi: 10.1155/2022/1450822. eCollection 2022.
Sign language plays a pivotal role in the lives of impaired people having speaking and hearing disabilities. They can convey messages using hand gesture movements. American Sign Language (ASL) recognition is challenging due to the increasing intra-class similarity and high complexity. This paper used a deep convolutional neural network for ASL alphabet recognition to overcome ASL recognition challenges. This paper presents an ASL recognition approach using a deep convolutional neural network. The performance of the DeepCNN model improves with the amount of given data; for this purpose, we applied the data augmentation technique to expand the size of training data from existing data artificially. According to the experiments, the proposed DeepCNN model provides consistent results for the ASL dataset. Experiments prove that the DeepCNN gives a better accuracy gain of 19.84%, 8.37%, 16.31%, 17.17%, 5.86%, and 3.26% as compared to various state-of-the-art approaches.
手语在有言语和听力障碍的残疾人的生活中起着至关重要的作用。他们可以通过手势动作传达信息。由于类内相似度增加和复杂性高，美国手语 (ASL) 识别具有挑战性。本文使用深度卷积神经网络来识别 ASL 字母，以克服 ASL 识别挑战。本文提出了一种使用深度卷积神经网络的 ASL 识别方法。随着提供的数据量的增加，DeepCNN 模型的性能会提高；为此，我们应用了数据扩充技术来人为地扩展现有数据的训练数据大小。根据实验，所提出的 DeepCNN 模型为 ASL 数据集提供了一致的结果。实验证明，与各种最先进的方法相比，DeepCNN 可以获得更好的准确性增益，分别为 19.84%、8.37%、16.31%、17.17%、5.86%和 3.26%。
