In the field of document forensics, ink analysis plays a crucial role in determining the authenticity of legal and historic documents and detecting forgery. Visual examination alone is insufficient for distinguishing visually similar inks, necessitating the use of advanced scientific techniques. This paper proposes an ink analysis technique based on hyperspectral imaging, which enables the examination of documents in hundreds of narrowly spaced spectral bands, revealing hidden details. The main objective of this study is to identify the number of distinct inks used in a document. Three clustering algorithms, namely k-means, Agglomerative, and c-means, are employed to estimate the number of inks present. The methodology involves data extraction, ink pixel segmentation, and ink number determination. The results demonstrate the effectiveness of the proposed technique in identifying ink clusters and distinguishing between different inks. The analysis of a hyperspectral cube dataset reveals variations in spectral reflectance across different bands and distinct spectral responses among the 12 lines, indicating the presence of multiple inks. The clustering algorithms successfully identify ink clusters, with k-means clustering showing superior classification performance. These findings contribute to the development of reliable methodologies for ink analysis using hyperspectral imaging, enhancing the

翻译：在文件检验领域，油墨分析对于判定法律与历史文件真伪、检测伪造行为至关重要。仅凭肉眼观察难以区分视觉相似的油墨，因此需要借助先进科学技术。本文提出一种基于高光谱成像的油墨分析技术，该技术可通过数百个窄光谱波段对文件进行检测，揭示隐藏细节。研究主要目标是识别文件中所用不同油墨的数量。采用k-means、凝聚层次聚类和c-means三种聚类算法估算油墨数量，方法涵盖数据提取、油墨像素分割及油墨数量判定等步骤。结果表明，所提技术能有效识别油墨簇并区分不同油墨。通过对高光谱立方体数据集的分析发现，不同波段的光谱反射率存在差异，12条线迹呈现不同光谱响应特征，表明存在多种油墨。三种聚类算法均成功识别油墨簇，其中k-means聚类展现出更优的分类性能。这些发现为建立基于高光谱成像的可靠油墨分析方法体系提供了支撑，有力提升了文件检验技术水平。