  The digital revolution has replaced the use of printed documents with their
digital counterparts. However, many applications require the use of both due to
several factors, including challenges of digital security, installation costs,
ease of use, and lack of digital expertise. Technological developments in the
digital domain have also resulted in the easy availability of high-quality
scanners, printers, and image editing software at lower prices. Miscreants
leverage such technology to develop forged documents that may go undetected in
vast volumes of printed documents. These developments mandate the research on
creating fast and accurate digital systems for source printer identification of
printed documents. We extensively analyze and propose a printer-specific
pooling that improves the performance of printer-specific local texture
descriptor on two datasets. The proposed pooling performs well using a simple
correlation-based prediction instead of a complex machine learning-based
classifier achieving improved performance under cross-font scenarios. The
proposed system achieves an average classification accuracy of 93.5%, 94.3%,
and 60.3% on documents printed in Arial, Times New Roman, and Comic Sans font
types respectively, when documents printed in only Cambria font are available
for training.
