  Given a sequence composed of a limit number of characters, we try to "read"
it as a "text". This involves to segment the sequence into "words". The
difficulty is to distinguish good segmentation from enormous number of random
ones.Aiming at revealing the nonrandomness of the sequence as strongly as
possible, by applying maximum likelihood method, we find a quantity called
Segmentation Entropy that can be used to fulfill the duty. Contrary to
commonplace where maximum entropy principle was applied to obtain good
solution, we choose to {\em minimize} the segmentation entropy to obtain good
segmentation. The concept developed in this letter can be used to study the
noncoding DNA sequences, e.g., for regulatory elements prediction, in eukaryote
genomes.
