  Handwritten text recognition is an active research area in the field of deep
learning and artificial intelligence to convert handwritten text into
machine-understandable. A lot of work has been done for other languages,
especially for English, but work for the Urdu language is very minimal due to
the cursive nature of Urdu characters. The need for Urdu HCR systems is
increasing because of the advancement of technology. In this paper, we propose
a ResNet18 model for handwritten text recognition using Urdu Nastaliq
Handwritten Dataset (UNHD) which contains 3,12000 words written by 500
candidates.
