Source Printer Identification using Printer Specific Pooling of Letter Descriptors

TL;DR

This paper presents a novel printer-specific pooling method for local texture descriptors that enhances source printer identification accuracy across different fonts, achieving over 93% accuracy on multiple font types with simple correlation-based prediction.

Contribution

It introduces a printer-specific pooling technique that improves local texture descriptor performance for printer identification, reducing reliance on complex classifiers and enhancing cross-font robustness.

Findings

Achieved 93.5% accuracy on Arial font documents

Achieved 94.3% accuracy on Times New Roman font documents

Achieved 60.3% accuracy on Comic Sans font documents

Abstract

The digital revolution has replaced the use of printed documents with their digital counterparts. However, many applications require the use of both due to several factors, including challenges of digital security, installation costs, ease of use, and lack of digital expertise. Technological developments in the digital domain have also resulted in the easy availability of high-quality scanners, printers, and image editing software at lower prices. Miscreants leverage such technology to develop forged documents that may go undetected in vast volumes of printed documents. These developments mandate the research on creating fast and accurate digital systems for source printer identification of printed documents. We extensively analyze and propose a printer-specific pooling that improves the performance of printer-specific local texture descriptor on two datasets. The proposed pooling performs well using a simple correlation-based prediction instead of a complex machine learning-based classifier achieving improved performance under cross-font scenarios. The proposed system achieves an average classification accuracy of 93.5%, 94.3%, and 60.3% on documents printed in Arial, Times New Roman, and Comic Sans font types respectively, when documents printed in only Cambria font are available for training.