  In this paper, we work on intra-variable handwriting, where the writing
samples of an individual can vary significantly. Such within-writer variation
throws a challenge for automatic writer inspection, where the state-of-the-art
methods do not perform well. To deal with intra-variability, we analyze the
idiosyncrasy in individual handwriting. We identify/verify the writer from
highly idiosyncratic text-patches. Such patches are detected using a deep
recurrent reinforcement learning-based architecture. An idiosyncratic score is
assigned to every patch, which is predicted by employing deep regression
analysis. For writer identification, we propose a deep neural architecture,
which makes the final decision by the idiosyncratic score-induced weighted
average of patch-based decisions. For writer verification, we propose two
algorithms for patch-fed deep feature aggregation, which assist in
authentication using a triplet network. The experiments were performed on two
databases, where we obtained encouraging results.
