Passive classification of source printer using text-line-level geometric distortion signatures from scanned images of printed documents

In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a severe threat. Ability to efficiently and reliably identify source printer of a printed document can help a lot in reducing this menace. During printing procedure, printer hardware introduces certain distortions in printed characters’ locations and shapes which are invisible to naked eyes. These distortions are referred as geometric distortions. Their profile (or signature) is generally unique for each printer and can be used for printer classification purpose. This paper proposes a set of features for characterizing text-line-level geometric distortions and presents a novel system to use them for identification of the origin of a printed document. Detailed experiments performed on a set of 14 printers demonstrate that the proposed system achieves performance of the state of the art system based on geometric distortion and gives much higher accuracy under small training size constraint. A classifier trained using 1 page/printer/font with 3 different fonts and 14 printers achieves 98.85% average classification accuracy.