Efficient Word Segmentation and Baseline Localization in Handwritten Documents Using Isothetic Covers

Introduction

A handwriting portrays the characteristics of an individual, and hence has been studied in numerous disciplines including experimental psychology, neuroscience, engineering, anthropology, forensic science, etc. (Plamondon, 1993; Plamondon & Leedham, 1990; Simner et al., 1994, 1996; Galen & Morasso, 1998; Galen & Stelmach, 1993; Wan et al., 1991). The analysis of handwritings has been quite important in recent times with the advancements of document digitization (Hole & Ragha, 2011; Saba, 2011; Terrades, 2010; Zhu et al., 2009), biometric authentication (Henniger & Franke, 2004; Hoque et al., 2008; Low et al., 2009; Makrushin, 2011; Schimke et al., 2005; Vielhauer, 2006; Vielhauer & Scheidat, 2005), forensic science (Franke & Köppen, 2001; Máadeed et al., 2008; Mahmoudi et al., 2009; Pervouchine et al., 2008), etc. The result of analysis strives to interpret, verify, and recognize a particular handwritten document. The most difficult problem in the area of handwriting recognition is segmentation of cursive handwriting. The infinitude of different types of human handwritings amidst the similarities in the shapes of different characters renders the problem even more difficult. Hence, over the last few years, various works have been presented for specific domains, e.g., Bengali character recognition (Majumdar & Chaudhuri, 2007; Parui et al., 2008), text line identification (Chaudhuri & Bera, 2009), numeral recognition (Bhattacharya & Chaudhuri, 2009), check sorting (Gorski et al., 1999), address reading (Srihari & Keubert, 1997), tax reading (Srihari et al., 1996), office automation (Gopisetty, 1996), automated postal system (Vajda et al., 2009), etc.