  There is an abundance of digitised texts available in Sanskrit. However, the
word segmentation task in such texts are challenging due to the issue of
'Sandhi'. In Sandhi, words in a sentence often fuse together to form a single
chunk of text, where the word delimiter vanishes and sounds at the word
boundaries undergo transformations, which is also reflected in the written
text. Here, we propose an approach that uses a deep sequence to sequence
(seq2seq) model that takes only the sandhied string as the input and predicts
the unsandhied string. The state of the art models are linguistically involved
and have external dependencies for the lexical and morphological analysis of
the input. Our model can be trained "overnight" and be used for production. In
spite of the knowledge lean approach, our system preforms better than the
current state of the art by gaining a percentage increase of 16.79 % than the
current state of the art.
