  Optical Character Recognition (OCR), the task of extracting textual
information from scanned documents is a vital and broadly used technology for
digitizing and indexing physical documents. Existing technologies perform well
for clean documents, but when the document is visually degraded, or when there
are non-textual elements, OCR quality can be greatly impacted, specifically due
to erroneous detections. In this paper we present an improved detection network
with a masking system to improve the quality of OCR performed on documents. By
filtering non-textual elements from the image we can utilize document-level OCR
to incorporate contextual information to improve OCR results. We perform a
unified evaluation on a publicly available dataset demonstrating the usefulness
and broad applicability of our method. Additionally, we present and make
publicly available our synthetic dataset with a unique hard-negative component
specifically tuned to improve detection results, and evaluate the benefits that
can be gained from its usage
