Automated Forensic Analysis for Ink Mismatch Detection in Hyperspectral Document Images

<p>Document authentication is a critical part of forensic analysis, which ensures the veracity of a document’s origins. A significant challenge in this field is the detection of ink mismatches, especially in instances of disproportionate ink distribution. This issue is effectively handled with hyperspectral images of documents, whereas traditional imaging techniques struggle to distinguish between visually similar inks. To address this problem, we introduce a new approach that leverages hyperspectral unmixing for ink mismatch detection in unbalanced clusters. The proposed method identifies unique spectral characteristics of different inks and their respective proportions, thus facilitating significant ink distinction. The proposed approach utilizes Elbow estimation and Silhouette coefficient for number of inks estimation and performs color segmentation for all unique ink types by employing k-means clustering and Gaussian mixture models (GMMs), outperforming existing methods in this regard which rely on prior knowledge about the number of inks used in the document. For abundance estimation, a unique method of dimensional reduction of HSI and channel wise analysis is proposed. We evaluate our approach on the iVision Handwritten Hyperspectral Images Data set (iVision HHID), a comprehensive and rich dataset that surpasses the commonlyused UWA Writing Inks Hyperspectral Images (WIHSI) database in size and diversity. Our results, in comparison with state-of-theart methods, demonstrate the effectiveness of our approach in hyperspectral ink mismatch detection. This paper thus promotes the application of hyperspectral imaging for document analysis and encourages further exploration toward automated questioned document examination.</p>