Identification of document paper using hybrid feature extraction

Abstract

Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray‐level co‐occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery.

Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

“…Various non-destructive methods for paper analysis and identification are currently in use. These methods encompass the comparison of physical characteristics such as basis weight, thickness, apparent density, surface roughness, brightness, opacity, and whiteness (Lee et al 2023). Table 2 provides details about the equipment and standards utilized for the evaluation of the physical and optical properties.…”

“…Despite the advances in information technology that have led to the concept of a paperless society, many tasks are still conducted using paper documents. Paper remains a crucial medium for recording various daily tasks and activities, including taking notes, jotting down memos, and formalizing important contracts (Ganzerla et al 2009;Lee et al 2023).…”

“…Meanwhile, owing to recent technological advances in office automation devices, document forgery has become more accessible, consequently underscoring the growing significance of paper document examination in fields such as forensic investigation and evidence analysis (Lee et al 2023). In South Korea, a substantial number of document forgery cases have been documented.…”

“…Over a three-year period starting in 2018, the statistics indicate that nearly ten thousand criminal cases of document forgery are reported annually (Choi et al 2018a;2018b). Hence, the development of robust forensic capabilities for detecting forgery becomes paramount (Kim et al 2016;Lee et al 2023).…”

Chemical and Imaging Analysis of Questioned Document Paper by FTIR, ICP and Video Spectral Comparator (VSC) for Forensic Discrimination

“…They proposed a novel approach for paper identification by using gray-level co-occurrence matrix (GLCM) approach and a convolutional neural network (CNN) that leads to achieving an accuracy of 97.66 %. [18] Lee et al combined spectroscopic techniques and machine learning modelling to evaluate and classify of copy papers according to their chemical properties. [19] Analytical techniques have gained immense attention to analyze the papers as forensic evidence, namely including XRD, [20,21] Raman spectroscopy, FTIR, [22] ICP-MS, [23,24] UV-Vis spectroscopy, [25] XPS, [26] image analysis, [27] XRF, [28] SEM, and so on.…”