Handwritten Text Segmentation Using Elastic Shape Analysis

Abstract

Segmentation of handwritten text into individual characters is an important step in many handwriting recognition tasks. In this paper, we present two segmentation algorithms based on elastic shape analysis of parameterized, planar curves. The shape analysis methodology provides matching, comparison and averaging of handwritten curves in a unified framework, which are very useful tools for designing segmentation algorithms. The first type of segmentation can be performed by splitting a full word into individual characters using a matching function. Another type of segmentation can be obtained by matching parts of the handwritten words to a given individual character. We validate the two proposed algorithms on real handwritten signatures and words coming from the SVC 2004 and the UNIPEN ICROW 2003 datasets. We show that the proposed methods are able to successfully segment text coming from highly variable handwriting styles.