Single Classifier-based Passive System for Source Printer Classification using Local Texture Features

TL;DR

This paper introduces a novel single classifier system that uses local texture features from all printed letters to accurately identify source printers in scanned documents, enhancing forgery detection.

Contribution

It presents a new method combining local texture patterns and a single classifier for printer classification without OCR, outperforming existing methods with fewer training pages.

Findings

Outperforms existing handcrafted feature-based methods

Requires fewer training pages due to all-letter analysis

Demonstrates shape independence in classification

Abstract

An important aspect of examining printed documents for potential forgeries and copyright infringement is the identification of source printer as it can be helpful for ascertaining the leak and detecting forged documents. This paper proposes a system for classification of source printer from scanned images of printed documents using all the printed letters simultaneously. This system uses local texture patterns based features and a single classifier for classifying all the printed letters. Letters are extracted from scanned images using connected component analysis followed by morphological filtering without the need of using an OCR. Each letter is sub-divided into a flat region and an edge region, and local tetra patterns are estimated separately for these two regions. A strategically constructed pooling technique is used to extract the final feature vectors. The proposed method has been tested on both a publicly available dataset of 10 printers and a new dataset of 18 printers scanned at a resolution of 600 dpi as well as 300 dpi printed in four different fonts. The results indicate shape independence property in the proposed method as using a single classifier it outperforms existing handcrafted feature-based methods and needs much smaller number of training pages by using all the printed letters.

Full text

See pages 1-last of draft.pdf