**OpenFst version 1.7.4 is now available for download.**

**OpenFst** is a library for constructing, combining, optimizing, and
searching *weighted finite-state transducers* (FSTs). Weighted
finite-state transducers are automata where each transition has an
input label, an output label, and a weight. The more familiar
finite-state acceptor is represented as a transducer with each
transition's input and output label equal. Finite-state acceptors
are used to represent sets of strings (specifically, *regular* or
*rational sets*); finite-state transducers are used to represent
binary relations between pairs of strings (specifically, *rational transductions*).
The weights can be used to represent the cost of taking a particular transition.

FSTs have key applications in speech recognition and synthesis,
machine translation, optical character recognition, pattern matching,
string processing, machine learning, information extraction and
retrieval among others. Often a weighted transducer is used to represent a
probabilistic model (e.g., an *n-gram model*,
*pronunciation model*). FSTs can be optimized by *determinization* and *minimization*,
models can be applied to hypothesis sets (also represented as automata) or cascaded
by finite-state *composition*, and the best results can be selected by *shortest-path* algorithms.

This library was developed by contributors from Google Research and NYU's Courant Institute. It is intended to be comprehensive, reliable, flexible, efficient, and to scale well. It is an open source project distributed under the Apache license.

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Topic revision: r110 - 2019-10-16 - KyleGorman

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