一个基于计算机视觉的系统,用于识别和分类乌尔都语手语数据集
Hira Zahid1, Munaf Rashid2, Sidra Abid Syed3
1Biomedical Engineering Department and Electrical Engineering Department, Ziauddin University, Karachi, Pakistan.
PeerJ. Computer science
|June 22, 2023
概括
研究人员开发了一种乌尔都语手语 (USL) 自动识别系统. 这种机器学习方法在识别USL号码方面实现了90%的准确性,弥合了聋人社区的沟通差距.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 语言学的语言学.
背景情况:
- 社交沟通至关重要,手语弥合了聋人和非聋人社区之间的差距.
- 全球大约有7000万聋人使用约300种不同的手语.
- 乌尔都语手语 (USL) 是一种用于日常互动的视觉通信系统.
研究的目的:
- 为了创建乌尔都语手语 (USL) 图像的数据集.
- 开发和测试用于自动识别美国手语的机器学习分类器.
- 为了评估不同机器学习模型的性能,用于USL编号分类.
主要方法:
- 收集和拍摄了1560张USL图像的数据集.
- 一个字袋 (BoW) 范式被用于自动识别.
- 使用支持向量机 (SVM),随机森林和K-最近邻居 (K-NN) 分类器与BoW直方图特征一起使用.
主要成果:
- 随机森林分类器实现了88%的准确性.
- 支持矢量机 (SVM) 分类器达到90%的准确性.
- K-最近邻居 (K-NN) 分类器的准确率达到84%.
结论:
- 拟议的机器学习策略有效地自动化了乌尔都语手语数字识别.
- 支持矢量机在测试的分类器中显示了最高的准确性.
- 该系统为提高聋人社区的通信可访问性提供了一个有希望的解决方案.
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