|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
USING CONSTRAINED SNAKES FOR FEATURE SPOTTING IN OFF-LINE CURSIVE SCRIPT
A. W. Senior and F. Fallside
Studies in the psychology of reading indicate that reading probably involves recognising features which are present in letters, such as loops, turns and straight strokes. If this is the case it is likely that recognising these features will be a useful technique for the machine recognition of cursive script. This paper describes a new method of detecting the presence of these features in a cursive handwritten word. The method uses constrained snakes which adapt to fit the maxima in the distance transform of a word image while retaining their basic shape. When the snake has settled into a potential minimum, its goodness-of-fit is used to determine whether a match has been found. The features located by this method are passed on to a `neural' network recogniser. Examples of the features recognised are shown and results for word recognition for this method, on a single-author database of scanned data with 825 word vocabulary are presented. These are followed by a conclusion and pointers to further work.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.
|| Search | CUED | Cambridge University ||
2005 Cambridge University Engineering Dept
Information provided by milab-maintainer