Using constrained snakes for feature spotting in off-line cursive script

Abstract

Studies in the psychology of reading indicate that reading probably involves recognizing features which are present in letters, such as loops, turns and straight strokes. If this is the case it is likely that recognizing these features will be a useful technique for the machine recognition of cursive script. A new method of detecting the presence of these features in a cursive handwritten word is described. 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 shape 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 recognizer. Examples of the features recognized are shown, and results for word recognition for this method on a single-author database of scanned data with 825 word vocabulary are presented. >