  State-of-the-art Natural Language Processing algorithms rely heavily on
efficient word segmentation. Urdu is amongst languages for which word
segmentation is a complex task as it exhibits space omission as well as space
insertion issues. This is partly due to the Arabic script which although
cursive in nature, consists of characters that have inherent joining and
non-joining attributes regardless of word boundary. This paper presents a word
segmentation system for Urdu which uses a Conditional Random Field sequence
modeler with orthographic, linguistic and morphological features. Our proposed
model automatically learns to predict white space as word boundary as well as
Zero Width Non-Joiner (ZWNJ) as sub-word boundary. Using a manually annotated
corpus, our model achieves F1 score of 0.97 for word boundary identification
and 0.85 for sub-word boundary identification tasks. We have made our code and
corpus publicly available to make our results reproducible.
