  An unconstrained crossword puzzle is a generalization of the constrained
crossword problem. In this problem, only the word vocabulary, and optionally
the grid dimensions are known. Hence, it not only requires the algorithm to
determine the word locations, but it also needs to come up with the grid
geometry. This paper discusses algorithmic strategies for automatic crossword
puzzle generation in such an unconstrained setting. The strategies proposed
cover the tasks of selection of words from a given vocabulary, selection of
grid sizes, grid resizing and adjustments, metrics for word fitting,
back-tracking techniques, and also clue generation. The strategies have been
formulated based on a study of the effect of word sequence permutation order on
grid fitting. An end-to-end algorithm that combines these strategies is
presented, and its performance is analyzed. The techniques have been found to
be successful in quickly producing well-packed puzzles of even large sizes.
Finally, a few example puzzles generated by our algorithm are also provided.
