历史手稿分析:使用智能特征选择和视觉转换器识别作家的深度学习系统
Merouane Boudraa1, Akram Bennour1, Mouaaz Nahas2
1Laboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa 12000, Algeria.
Journal of imaging
|June 25, 2025
概括
这项研究引入了一个使用视觉转换器识别历史手稿作者的深度学习系统. 该方法增强了历史文档分析和作者识别准确度.
科学领域:
- 计算机科学 计算机科学
- 数字人文学科 数字人文学科
- 历史文档分析 历史文档分析
背景情况:
- 识别历史手稿作者对于历史研究和解决团至关重要.
- 现有的方法可能缺乏复杂历史文档分析所需的精度.
研究的目的:
- 开发和评估用于历史手稿作者识别的深度学习系统.
- 评估视觉转换器和特征选择技术在这个领域的有效性.
主要方法:
- 文件预处理包括双边过和Otsu值.
- 特性提取使用了FAST探测器和k-means集群用于统一的补丁.
- 视觉变压器模型被用于分类手写模式.
主要成果:
- 该系统在按作者分类历史手稿方面表现出强大的性能.
- 视觉转换器在从手稿数据中学习复杂模式方面表现出卓越的能力.
- 这种方法在ICDAR 2017年数据集上胜过了最先进的方法.
结论:
- 开发的深度学习系统是历史手稿分析的强大工具.
- 视觉变压器代表了自动化历史文档分析的重大进步.
- 这项研究为历史作家识别挑战提供了一种新的解决方案.
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