论文标题
使用卷积神经网络和转移学习对瑞典手语的解释
Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer Learning
论文作者
论文摘要
符号语言的自动解释是一项具有挑战性的任务，因为它需要使用高级视觉和高级运动处理系统来提供准确的图像感知。在本文中，我们使用卷积神经网络（CNN）和转移学习，以使计算机能够解释瑞典手语（SSL）手写字母的迹象。我们的模型包括实现预训练的InceptionV3网络，以及Mini Batch梯度下降优化算法的使用。我们依靠在模型的预训练期间转移学习及其数据。基于8个研究对象和9,400张图像的模型的最终准确性为85％。我们的结果表明，CNN的使用是一种解释标志语言的有前途的方法，尽管使用了小型培训数据集，但可以将转移学习用于实现高测试精度。此外，我们描述了模型的实现详细信息，以将标志解释为用户友好的Web应用程序。
The automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning in order to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consist of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9,400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.
