  Digit, letter and word recognition for a particular script has various
applications in todays commercial contexts. Nevertheless, only a limited number
of relevant studies have dealt with Persian scripts. In this paper, deep neural
networks are utilized through various DensNet architectures, as well as the
Xception, are adopted, modified and further boosted through data augmentation
and test time augmentation, in order to come up with an optical character
recognition accounting for the particularities of the Persian language and the
corresponding handwritings. Taking advantage of dividing the databases to
training, validation and test sets, as well as k-fold cross validation, the
comparison of the proposed method with various state-of-the-art alternatives is
performed on the basis of the HODA and Sadri databases, which offer the most
comprehensive collection of samples in terms of the various handwriting styles
possessed by different human beings, as well as different forms each letter may
take, which depend on its position within a word. On the HODA database, we
achieve recognition rates of 99.72% and 89.99% for digits and characters, being
99.72%, 98.32% and 98.82% for digits, characters and words from the Sadri
database, respectively.
