Online Handwriting Recognition Using Levenshtein Distance Metric

In this article, we propose a novel scheme for online handwritten character recognition based on Levenshtein distance metric. Both shape and position information are considered in our feature representation scheme. The shape information is encoded by a string of quantized values of angular displacements between successive sample points along the trajectory of the handwritten character. The consecutive occurrences of same value in such a string are removed retaining only one of them. Next, each element in the resulting string is assigned an integral weight value proportional to the length of the segment of the trajectory represented by the corresponding element. Similarly, position information is encoded by another string of quantized positional information along with their respective weight values. We formulated a distance function based on Levenshtein metric to compute the similarity between an unknown character sample and each training sample. Here, we have also studied the effect of pruning the training sample set based on the above distance between individual training samples of the same character class. The proposed approach has been simulated on different publicly available sample databases of online handwritten characters. The recognition accuracies are acceptable.