  Some historical and more recent printed documents have been scanned or stored
at very low resolutions, such as 60 dpi. Though such scans are relatively easy
for humans to read, they still present significant challenges for optical
character recognition (OCR) systems. The current state-of-the art is to use
super-resolution to reconstruct an approximation of the original
high-resolution image and to feed this into a standard OCR system. Our novel
end-to-end method bypasses the super-resolution step and produces better OCR
results. This approach is inspired from our understanding of the human visual
system, and builds on established neural networks for performing OCR.
  Our experiments have shown that it is possible to perform OCR on 60 dpi
scanned images of English text, which is a significantly lower resolution than
the state-of-the-art, and we achieved a mean character level accuracy (CLA) of
99.7% and word level accuracy (WLA) of 98.9% across a set of about 1000 pages
of 60 dpi text in a wide range of fonts. For 75 dpi images, the mean CLA was
99.9% and the mean WLA was 99.4% on the same sample of texts. We make our code
and data (including a set of low-resolution images with their ground truths)
publicly available as a benchmark for future work in this field.
