Using Strings for On-Line Handwriting Shape Matching: A New Weighted Edit Distance

Abstract

AbstractEdit Distance has been widely studied and successfully applied in a large variety of application domains and many techniques based on this concept have been proposed in the literature. These techniques share the property that, in case of patterns having different lengths, a number of symbols are introduced in the shortest one, or deleted from the longest one, until both patterns have the same length. In case of applications in which strings are used for shape description, however, this property may introduce distortions in the shape, resulting in a distance measure not reflecting the perceived similarity between the shapes to compare. Moving from this consideration, we propose a new edit distance, called Weighted Edit Distance that does not require the introduction or the deletion of any symbol. Preliminary experiments performed by comparing our technique with the Normalized Edit Distance and the Markov Edit Distance have shown very encouraging results.KeywordsMachine IntelligenceEdit DistanceCurvature MaximumSegmentation PointChinese Character RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.