Research on Combining Hyperspectral Imaging and Deep Residual Shrinkage Network for Ink Identification of Question Document Handwriting
In the cases of economic crimes and various civil disputes,handwriting ink identification is of great significance for consensus identification in questioned documents,and the related research has been an important topic in the field of court science.In view of the low efficiency and accuracy of traditional methods,a new method combining hyperspectral imaging with deep residual shrinkage network was pro-posed for rapid and non-destructive identification of ink types.First,the hyperspectral images of 30 black signature pen inks of different brands and models were collected.The hyperspectral images of each neu-tral pen ink were segmented,and the handwriting regions were extracted for 10 × 10 pixel fusion to obtain a total of 13 942 pixel points of reflectance data as the sample set.Second,a one-dimensional deep resid-ual shrinkage network model suitable for processing hyperspectral data was proposed by combining residu-al network,soft valorization and attention mechanism.Meanwhile,this model was compared with convo-lutional neural network and traditional machine learning models.The experimental results showed that the test accuracy of pixel reflectance of support vector machine,logistic regression and random forest was 59.1％,57.8％and 51.7％,respectively,and that of convolutional neural network was 64.2％.The value of loss function dropped to 1.536.While the validation recognition rate of deep residual shrinkage network was the highest,reaching 75.4％,and the value of loss function finally dropped to 0.920,reac-hing convergence.The results showed that the spectral detection method had the advantage of non-de-structive,fast imaging and simple operation.The proposed deep residual shrinkage network has obvious advantages in classification effect and accuracy of handwriting inks and can realize the deep mining of hy-perspectral data and accurate classification of black signature pen ink types,which provides a technical support for the identification of questioned document in forensic science.
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