  Digitization of newspapers is of interest for many reasons including
preservation of history, accessibility and search ability, etc. While
digitization of documents such as scientific articles and magazines is
prevalent in literature, one of the main challenges for digitization of
newspaper lies in its complex layout (e.g. articles spanning multiple columns,
text interrupted by images) analysis, which is necessary to preserve human
read-order. This work provides a major breakthrough in the digitization of
newspapers on three fronts: first, releasing a dataset of 3000 fully-annotated,
real-world newspaper images from 21 different U.S. states representing an
extensive variety of complex layouts for document layout analysis; second,
proposing layout segmentation as a precursor to existing optical character
recognition (OCR) engines, where multiple state-of-the-art image segmentation
models and several post-processing methods are explored for document layout
segmentation; third, providing a thorough and structured evaluation protocol
for isolated layout segmentation and end-to-end OCR.
