  The contributions in this article are two-fold. First, we introduce a new
hand-written digit data set that we collected. It contains high-resolution
images of hand-written The contributions in this article are two-fold. First,
we introduce a new handwritten digit data set that we collected. It contains
high-resolution images of handwritten digits together with various writer
characteristics which are not available in the well-known MNIST database. The
multiple writer characteristics gathered are a novelty of our data set and
create new research opportunities. The data set is publicly available online.
Second, we analyse this new data set. We begin with simple supervised tasks. We
assess the predictability of the writer characteristics gathered, the effect of
using some of those characteristics as predictors in classification task and
the effect of higher resolution images on classification accuracy. We also
explore semi-supervised applications; we can leverage the high quantity of
handwritten digits data sets already existing online to improve the accuracy of
various classifications task with noticeable success. Finally, we also
demonstrate the generative perspective offered by this new data set; we are
able to generate images that mimics the writing style of specific writers. The
data set has unique and distinct features and our analysis establishes
benchmarks and showcases some of the new opportunities made possible with this
new data set.
