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标语检测数据集:基于人工智能的识别系统的资源

Bindu Garg1, Manisha Kasar1, Priyanka Paygude1

  • 1Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.

Data in brief
|June 19, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于自动标语检测的深度学习模型,在分类手势方面实现了高精度. 开发的系统显示了对现实世界应用的巨大潜力,使聋人社区受益.

关键词:
美国手语是美国手语.卷积神经网络是一个卷积神经网络.深度学习 (Deep Learning) 是一种深度学习.标记语言识别 标记语言识别

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 手语对于聋人和听力障碍者之间的沟通至关重要.
  • 自动手语检测系统可以显著提高可访问性和包容性.

研究的目的:

  • 开发和评估用于自动识别手语的深度学习模型.
  • 用卷积神经网络 (CNN) 将手势分类为不同的标志.

主要方法:

  • 一个数据集由26,000个手语图像组成,每个字母为3000个图像.
  • 数据预处理包括大小调整,灰度转换,正常化和增强技术 (旋转,翻转,缩放,亮度调整,高斯噪声).
  • 数据集被分为70%的培训,15%的验证和15%的测试集,用于训练CNN模型.

主要成果:

  • 美国有线电视新闻网的模型在分类手势方面表现出了很高的准确性.
  • 数据增强技术提高了对各种环境条件的模型稳定性.
  • 多样化的数据集,包括各种参与者和受控收集方法,增强了模型的概括性.

结论:

  • 开发的深度学习模型显示了对现实世界应用的巨大潜力.
  • 这项技术可以成为聋人社区有价值的可访问性工具.
  • 该系统可以用于教育目的和实时手语识别.