Comparative performance study of wore segmentation techniques for handwritten Odia documents

Abstract

Word segmentation of handwritten documents is a vital step in the Optical Character Recognition system as its accuracy greatly influences the overall recognition performance. In the literature, various methods have been proposed for word segmentation of handwritten documents of various languages. However, it is observed that for Odia, which is an important Indian language, very little work has been reported on word segmentation. Hence, the objective of this paper is to employ two standard existing methods to segment words of Odia handwritten documents and compare the segmentation performance of these methods with the lone Water Reservoir Algorithm available in the literature and finally rank those methods based on their segmentation performance. It is observed that out of three methods, the Tree Structure method performs the best comparing four different performance measures. � 2016 IEEE.