论文标题
使用转移学习实时转换文本转换的手语
Sign Language to Text Conversion in Real Time using Transfer Learning
论文作者
论文摘要
听力受损的世界人民在沟通中遇到了许多障碍，并要求口译员理解一个人在说什么。一直存在科学研究，现有模型缺乏做出准确预测的能力。因此，我们提出了一个深入学习模型，该模型在ASL（即美国手语）上训练，该模型将以ASL为输入的形式采取行动，并将其转化为文本。为了实现翻译，使用了基于VGG16体系结构的卷积神经网络模型和转移学习模型。从CNN的94％到转移学习的98.7％，准确性提高了5％。还建立了具有深度学习模型的应用程序。
The people in the world who are hearing impaired face many obstacles in communication and require an interpreter to comprehend what a person is saying. There has been constant scientific research and the existing models lack the ability to make accurate predictions. So we propose a deep learning model trained on ASL i.e. American Sign Language which will take actions in the form of ASL as input and translate it into text. To achieve the translation a Convolution Neural Network model and a transfer learning model based on the VGG16 architecture are used. There has been an improvement in accuracy from 94% of CNN to 98.7% of Transfer Learning, an improvement of 5%. An application with the deep learning model integrated has also been built.
