  Neural machine translation (NMT) is a recent and effective technique which
led to remarkable improvements in comparison of conventional machine
translation techniques. Proposed neural machine translation model developed for
the Gujarati language contains encoder-decoder with attention mechanism. In
India, almost all the languages are originated from their ancestral language -
Sanskrit. They are having inevitable similarities including lexical and named
entity similarity. Translating into Indic languages is always be a challenging
task. In this paper, we have presented the neural machine translation system
(NMT) that can efficiently translate Indic languages like Hindi and Gujarati
that together covers more than 58.49 percentage of total speakers in the
country. We have compared the performance of our NMT model with automatic
evaluation matrices such as BLEU, perplexity and TER matrix. The comparison of
our network with Google translate is also presented where it outperformed with
a margin of 6 BLEU score on English-Gujarati translation.
