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Article Abstract

To address the technical challenges associated with determining the chronological order of overlapping stamps and textual content in forensic document examination, this study proposes a novel non-destructive method that integrates hyperspectral imaging (HSI) with convolutional neural networks (CNNs). A multi-type cross-sequence dataset was constructed, comprising 60 samples of handwriting-stamp sequences and 20 samples of printed text-stamp sequences, all subjected to six months of natural aging. Spectral responses were collected across the 400-1000 nm range in the overlapping regions. To suppress spectral noise, multiplicative scatter correction (MSC) was employed as a preprocessing step. The proposed dual-layer CNN architecture consists of an initial convolutional layer with 64 3 × 3 kernels followed by 2 × 2 max pooling, a second convolutional layer with 128 3 × 3 kernels and another 2 × 2 pooling layer, followed by a fully connected layer with 256 neurons that integrates spatial-spectral features, culminating in a four-class classification using Softmax. The model was trained over 150 epochs using the Adam optimizer (learning rate = 0.001) and L2 regularization ( = 0.001). The approach accurately distinguished between the chronological order of laser-printed toner, gel pen ink, and traditional/photo-sensitive stamp inks. Experimental results demonstrate a classification accuracy of 97.62% (AUC = 0.9965) on the printed text dataset and 96.67% (AUC = 0.9921) on the handwriting dataset, outperforming both extreme learning machine (ELM) (90.42%) and long short-term memory (LSTM) (96.43%) baselines. All pure (non-overlapping) samples were correctly classified with 100% accuracy. Feature analysis confirms the CNN's ability to extract highly discriminative spatial features, effectively overcoming the subjectivity and material-damaging limitations of traditional microscopic techniques.

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http://dx.doi.org/10.1039/d5ay01131kDOI Listing

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