  The knowledge of source printer can help in printed text document
authentication, copyright ownership, and provide important clues about the
author of a fraudulent document along with his/her potential means and motives.
Development of automated systems for classifying printed documents based on
their source printer, using image processing techniques, is gaining a lot of
attention in multimedia forensics. Currently, state-of-the-art systems require
that the font of letters present in test documents of unknown origin must be
available in those used for training the classifier. In this work, we attempt
to take the first step towards overcoming this limitation. Specifically, we
introduce a novel printer specific local texture descriptor. The highlight of
our technique is the use of encoding and regrouping strategy based on small
linear-shaped structures composed of pixels having similar intensity and
gradient. The results of experiments performed on two separate datasets show
that: 1) on a publicly available dataset, the proposed method outperforms
state-of-the-art algorithms for characters printed in the same font, and 2) on
another dataset\footnote{Code and dataset will be made publicly available with
published version of this paper.} having documents printed in four different
fonts, the proposed method correctly classifies all test samples when
sufficient training data is available in same font setup. In addition, it
outperforms state-of-the-art methods for cross font experiments. Moreover, it
reduces the confusion between the printers of same brand and model.
