Title: Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents,
Abstract: 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 serious threat. Ability to
easily 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, referred
as geometric distortion signatures and presents a novel system to use them for
identification of the origin of a printed document. Detailed experiments
performed on a set of thirteen printers demonstrate that the proposed system
achieves state of the art performance and gives much higher accuracy under
small training size constraint. For four training and six test pages of three
different fonts, the proposed method gives 99\% classification accuracy.