阿拉伯手稿分类的深度学习方法
Lutfieh S Al-Homed1, Kamal M Jambi1, Hassanin M Al-Barhamtoshy2
1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一个深度学习框架,用于使用退化目录卡识别未知的古代阿拉伯手稿. 该方法达到92.5%的准确性,明显优于传统的光学字符识别 (OCR) 和图像分类技术.
科学领域:
- 数字人文学科 数字人文学科
- 信息科学 信息科学 信息科学
- 计算机视觉 计算机视觉
背景情况:
- 古老的阿拉伯手稿是重要的文化遗产,但遭受了退化.
- 退化导致关键元数据 (作者,标题,主题) 的丢失.
- 相关的目录卡也会退化,阻碍识别工作.
研究的目的:
- 开发一个框架来识别未知的古老阿拉伯手稿.
- 为了克服退化的目录卡所带来的挑战.
- 为了提高手稿识别的准确性,超越传统方法.
主要方法:
- 一个用于处理退化目录卡的深度学习架构.
- 技术包括兴趣区域识别,旋转校正,特征提取和文本分类.
- 该框架旨在从损坏的卡片中找到,提取和分类文本.
主要成果:
- 拟议的深度学习框架实现了92.5%的准确率.
- 这远远超过了经典的图像分类 (83.5%) 和单独的OCR (81.5%).
- 证明了深度学习方法对退化文档分析的有效性.
结论:
- 开发的框架有效地识别了未知的古老阿拉伯手稿.
- 深度学习为分析退化历史文档提供了卓越的解决方案.
- 通过先进的计算方法,可以加强保护和访问文化遗产.
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