Work

Laser-printed document classification using random forest and gray prediction models

Year: 2025

Type: article

Abstract: This paper presents a classification method for laser-printed documents, integrating the random forest algorithm with the gray prediction model to enhance the accuracy and reliability of forensic document examination. The study utilizes 14 laser printers from five different brands as experimental subjects and extracts 14 key feature parameters such as gray mean, contrast, and distribution symmetry using the ImageXpert analysis system. Classification is done by the random forest algorithm, and the gray prediction model is used to enhance accuracy of classification. Finally, experimental results show that the proposed method achieves high precision or accuracy (96.00% for Chinese characters with fewer strokes and 92.86% for punctuation marks [periods]) for the character and punctuation classification. Compared to traditional classification methods, this approach exhibits superior stability and accuracy. The findings highlight the advantages of non-destructive analysis, efficient classification, and robustness, underscoring its potential as a valuable technological tool for forensic document examination in legal contexts.

Source: iScience

DOI: 10.1016/j.isci.2025.114131

Authors: Yinxuan Qu, Chenyang Yu, Chunhui Li, Yuzhu Yang

Institutions: Ministry of Public Security of the People's Republic of China, China People's Public Security University

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,240

Funders: Ministry of Public Security of the People's Republic of China, People's Public Security University of China, Fundamental Research Funds for the Central Universities