Elastic Matching of Online Handwritten Tamil and Telugu Scripts Using Local Features

This paper describes character based elastic matching using local features for recognizing online handwritten data. Dynamic time warping (DTW) has been used with four different feature sets: x-y features, shape context (SC) and tangent angle (TA) features, generalized shape context feature (GSC) and the fourth set containing x-y, normalized first and second derivatives and curvature features. Nearest neighborhood classifier with DTW distance was used as the classifier. In comparison, the SC and TA feature set was found to be the slowest and the fourth set was best among all in the recognition rate. The results have been compiled for the online handwritten Tamil and Telugu data. On Telugu data we obtained an accuracy of 90.6% with a speed of 0.166 symbols/sec. To increase the speed we have proposed a 2-stage recognition scheme using which we obtained accuracy of 89.77% but with a speed of 3.977 symbols/sec.