Paper fingerprint by forming fabric: analysis of periodic marks with 2D lab formation sensor and machine learning for forensic paper-identification.

Weave and drainage marks on paper surfaces derived from forming fabrics installed in paper machines were examined. Non-destructive differentiation of copy papers from various manufacturers was performed by a 2D Lab Formation Sensor. Individual characteristics such as angles and wavelengths formed by the fabric were selected as parameters for the analysis. These parameters were sampled from a total of 500 papers: 50 sheets of paper from each of the 10 groups by copy paper manufacturers. Using machine learning algorithms such as partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), and naive bayes (NB) models to analyze these parameters allows for the identification of copy paper groups by manufacturers. The NB classifier demonstrated superior performance, achieving an accuracy of 0.973 even with a 50 % reduction in input variables. This study shows that periodic marks on paper surfaces can act as important indicators of the forming fabric and the distinct features of a paper-making machine. In general, this paper proposes that the 2D Lab Formation Sensor along with machine learning models can be utilized for non-destructive copy paper categorization. This identification method could be broadened to determine information regarding suspicious documents for forensic identification purposes. [ABSTRACT FROM AUTHOR]