在历史文档中使用超光谱成像和机器学习方法进行墨水分类
Ana Belén López-Baldomero1, Marco Buzzelli2, Francisco Moronta-Montero1
1Department of Optics, University of Granada, Faculty of Sciences, Campus Fuentenueva, s/n, Granada, 18071, Spain.
概括
超光谱成像和机器学习准确地识别历史油墨,即使有退化. 一个深度学习模型实现了98%的F1得分,有助于手稿的保存.
科学领域:
- 分析化学 分析化学
- 影像科学 影像科学
- 计算科学 计算科学
背景情况:
- 由于降解和光谱重叠,墨水识别具有挑战性.
- 超光谱成像为光谱分析提供了一种非侵入性的方法.
- 机器学习可以对复杂的光谱数据进行分类.
研究的目的:
- 使用超光谱成像,对金属酸盐,含碳和不含碳的油墨进行分类.
- 评估传统和深度学习模型的墨水分类准确性.
- 评估历史文档的非侵入性墨水分析的可行性.
主要方法:
- 在多个系统中利用过光谱成像.
- 应用了六种监督机器学习模型:SVM,KNN,LDA,RF,PLSDA和一个DL模型.
- 综合数据融合,样本提取,基础真相创建和后处理.
主要成果:
- 所有模型都在模拟样本上实现了>90%的微平均精度.
- 深度学习模型获得了最高的F1分数 (98%).
- 传统模型在历史案例研究文件上表现更好.
结论:
- 超光谱成像与机器学习相结合,有效用于非侵入性墨水识别.
- 这种方法即使在降解材料和光谱重叠的情况下也是可靠的.
- 这项技术支持保存和分析历史手稿.
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