  Computationally analyzing Sanskrit texts requires proper segmentation in the
initial stages. There have been various tools developed for Sanskrit text
segmentation. Of these, G\'erard Huet's Reader in the Sanskrit Heritage Engine
analyzes the input text and segments it based on the word parameters - phases
like iic, ifc, Pr, Subst, etc., and sandhi (or transition) that takes place at
the end of a word with the initial part of the next word. And it enlists all
the possible solutions differentiating them with the help of the phases. The
phases and their analyses have their use in the domain of sentential parsers.
In segmentation, though, they are not used beyond deciding whether the words
formed with the phases are morphologically valid. This paper tries to modify
the above segmenter by ignoring the phase details (except for a few cases), and
also proposes a probability function to prioritize the list of solutions to
bring up the most valid solutions at the top.
