基于注意力的混合深度学习模型与CSFOA优化和G-TverskyUNet3+用于阿拉伯手语手语识别
Ahmed A Mohamed1, Abdullah Al-Saleh2, Sunil Kumar Sharma3
1Department of Computer Science, College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia.
Scientific reports
|June 26, 2025
概括
一个新的DeepArabianSignNet模型通过集成先进的深度学习技术来增强阿拉伯手语 (ArSL) 的识别. 这种方法显著提高了聋人社区理解视觉手动沟通的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 阿拉伯手语 (ArSL) 对于阿拉伯语地区的聋人之间沟通至关重要.
- 现有的ARSL识别方法在准确性和特征提取方面存在局限性.
- 准确的ARSL认可对于教育,医疗保健和社会包容至关重要.
研究的目的:
- 介绍DeepArabianSignNet,这是一个用于增强阿拉伯手语手语识别的新型模型.
- 克服以前的ARSL认可方法的局限性.
- 为了提高ARSL识别的准确性和特征捕获能力.
主要方法:
- 拟议的DeepArabianSignNet模型结合了DenseNet,EfficientNet和基于注意力的深度ResNet.
- 使用G-TverskyUNet3+来检测ArSL图像中的感兴趣区域.
- 采用交叉种子森林优化算法来进行特征选择 (纹理,颜色,深度学习).
主要成果:
- 该模型在两个数据库上进行了评估,培训率分别为70%和80%.
- 数据库2实现了高精度:0.97675 (70%的培训) 和0.98376 (80%的培训).
- 在阿拉伯手语手语识别方面取得了显著的改进.
结论:
- 在提高阿拉伯手语手语识别方面,DeepArabianSignNet被证明是有效的.
- 拟议的模型解决了以前的精度和特征提取挑战.
- 这项工作有助于为聋人社区推进辅助技术.
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