  Historical Document Image Binarization is a well-known segmentation problem
in image processing. Despite ubiquity, traditional thresholding algorithms
achieved limited success on severely degraded document images. With the advent
of deep learning, several segmentation models were proposed that made
significant progress in the field but were limited by the unavailability of
large training datasets. To mitigate this problem, we have proposed a novel
two-stage framework -- the first of which comprises a generator that generates
degraded samples using variational inference and the second being a CNN-based
binarization network that trains on the generated data. We evaluated our
framework on a range of DIBCO datasets, where it achieved competitive results
against previous state-of-the-art methods.
