Levenshtein distance metric based holistic handwritten word recognition

The rapid spread of pen-based digital devices and touch screen devices coupled with their affordability, and capability to take technology and digitization of data to the grassroots, has made online handwriting recognition an active field of research. The relevance of research on on-line handwriting recognition for Indian scripts is particularly high because the challenges posed by Indian scripts are different from other scripts. This is not only because of their extremely large alphabet size but also because the inter class variability among several classes is very small. In this article, we introduce a limited vocabulary online unconstrained handwritten Bangla (a major Indian script) word recognizer based on a novel word level feature representation. Here, we consider three different features extracted from a word sample and three event strings are generated corresponding to these three features. A distance function is formulated which uses the Levenshtein distance metric to compute the distance between two triplets of event strings representing two word samples. The nearest neighbour scheme is used to classify the input sample. We have simulated the proposed approach on vocabularies of varying sizes and the recognition performances are encouraging.