Experimental results on improved handwritten word recognition using the Levenshtein metric

Abstract

Using a Levenshtein-like distance for dynamic handwriting recognition of discrete words, i.e. characters should not overlap, we demonstrate that the word recognition rate can be greatly improved by enhancing the nature of the information provided by the character recognition classifier to the lexical processor. A Radial Basis Function (RBF) classifier is used to provide accurate substitution costs to a Levenshtein metric lexical search scheme. We report experimental results that demonstrate a clear advantage of this method over the traditional use of the classifier confusion matrix for substitution cost estimation. We conjecture that this is probably related to the highly multi-modal and ambiguous nature of the handwritten character classification problem.