Combination of Features for Efficient Recognition of Offline Handwritten Devanagari Words

TLDR: The recent study of a novel combination of two feature vectors for holistic recognition of offline handwritten word images shows sharp improvement in recognition accuracy over the use of any of the individual feature representation schemes.

Abstract: In this article, we describe our recent study of a novel combination of two feature vectors for holistic recognition of offline handwritten word images. In the literature, both contour and skeleton based feature representations have been studied for offline handwriting recognition purpose. However, to the best of our knowledge, there is no such study in which combination of the two feature representations have been considered for the purpose. In the proposed recognition scheme, we use multiclass SVM as the classifier. We have implemented the proposed approach for holistic recognition of Devanagari handwritten town names and tested its performance on a large handwritten word sample database of 100 Indian town names written in Devanagari. Experimental results show sharp improvement in recognition accuracy over the use of any of the individual feature representation schemes. The proposed approach is script independent and can be used for development of a holistic handwritten word image recognition of any script.