The automatic recognition of hand-written text has been a goal
for over thirty five years. The highly ambiguous nature of cursive
writing (with high variability between not only different writers, but
even between different samples from the same writer), means that
systems based only on visual information are prone to errors.
It is suggested that the application of linguistic knowledge to
the recognition task may improve recognition accuracy. If a low-level
(pattern recognition based) recogniser produces a candidate lattice
(i.e. a directed graph giving a number of alternatives at each word
position in a sentence), then linguistic knowledge can be used to find
the 'best' path through the lattice.
There are many forms of linguistic knowledge that may be used
to this end. This thesis looks specifically at the use of collocation as a
source of linguistic knowledge. Collocation describes the statistical
tendency of certain words to co-occur in a language, within a defined
range. It is suggested that this tendency may be exploited to aid
automatic text recognition.
The construction and use of a post-processing system
incorporating collocational knowledge is described, as are a number
of experiments designed to test the effectiveness of collocation as an
aid to text recognition. The results of these experiments suggest that
collocational statistics may be a useful form of knowledge for this
application and that further research may produce a system of real
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.