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In the questioned document, the examination of stamp-pad ink is crucial scientific evidence to discern the difference between genuine and forged documents. In this study, a new method for rapid and non-destructive identification of types of stamppad inks by combining hyperspectral imaging (HSI) technology and deep learning was developed. Twenty stamp-pad inks of different brands and models were collected and numbered in turn, and then, each of them was sealed six times repeatedly on the A4 printing paper for the test. After that, the hyperspectral imager was used to collect the hyperspectral images and the reflectance spectral data were obtained after pixel fusion. Principal component analysis (PCA) and non-negative matrix factorization (NMF) were used to deal with the dataset, but visual results were not good. Then, back propagation neural network (BPNN) and one-dimensional convolutional neural network (1D-CNN) were constructed and their merits and drawbacks were compared.The final loss function of the BPNN of training set and validation set was stable at 0.27 and 0.42, and the classification accuracy of the training set and validation set reached 90.02% and 83.99%, respectively. Compared with the BPNN, the 1D-CNN had better stability and efficiency for the classification. The loss function of the training set and validation set was as low as 0.068 and 0.075, and the final classification accuracy reached 98.30% and 97.94%, respectively. Therefore, the combination of hyperspectral imaging technology and 1D-CNN represents a potentially simple, nondestructive, and rapid method for stamp-pad inks detection and classification.

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The identification of seal authenticity is an important part of document inspection. Confocal laser Raman spectroscopy combined with convolutional neural (CNN) and recurrent neural (RNN) networks was used to distinguish red stamp‐pad ink of different brands and aging. A total of 536 spectral samples from 16 brands were collected in this study and 53,600 amplification data samples were obtained by adding noise. The joint neural network has significant classification performance compared to partial least squares (PLS) discriminant and common CNN. In the three kinds of stamp‐pad inks, photosensitive, atomic, and common, the recognition rate for different brands and aging both reached 100%.