Single Classifier-Based Passive System for Source Printer Classification Using Local Texture Features

An important aspect of examining printed documents for potential forgeries and copyright infringement is the identification of the source printer as it can be helpful for detecting forged documents and ascertaining the leak. This paper proposes a system for classification of source printer from scanned images of printed documents using all the printed letters simultaneously. The proposed 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 optical character recognition. 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 data set of ten printers, and a new data set of 18 printers scanned at a resolution of 600 as well as 300 dpi printed in four different fonts. The results indicate that the proposed system is capable of simultaneously dealing with all the printed letters and using a single classifier outperforms existing handcrafted feature-based methods. To achieve accuracies similar to that of state-of-art methods, it needs a much smaller number of training pages by using all the printed letters.