  In this paper, we introduce a data-driven approach to transliterating Uzbek
dictionary words from the Cyrillic script into the Latin script, and vice
versa. We heuristically align characters of words in the source script with
sub-strings of the corresponding words in the target script and train a
decision tree classifier that learns these alignments. On the test set, our
Cyrillic to Latin model achieves a character level micro-averaged F1 score of
0.9992, and our Latin to Cyrillic model achieves the score of 0.9959. Our
contribution is a novel method of producing machine transliterated texts for
the low-resource Uzbek language.
