State of the art in off-line writer identification of handwritten text and survey of writer identification of Arabic text

Abstract

In this paper we present the state of the art in writer identification and verification of handwritten text. In addition, a special and extensive survey of writer identification and verification of Arabic handwritten text is also included. Different feature extraction techniques are addressed showing the different research groups' efforts as well as individual efforts. The different classification approaches, e.g. minimum distance classifiers and statistical classifiers, used for identification by writer and verification by different groups and individuals are presented. Identification results of surveyed publications are investigated and tabulated for ease of reference. Examples of writer identification and verification of others languages are addressed. An extensive survey of databases used in writer identification and verification for Latin and Arabic text is presented. Conclusions relevant to writer identification of Arabic text are discussed and future directions stated.

The lognormal handwriter: learning, performing, and declining

“…These studies were conducted using bottom-up or top-down approaches and exploit more or less sophisticated technologies, from complex fMRI investigations (Katanoda et al, 2001; Seitz, 2009; Richards et al, 2011; Shah et al, 2013) to simple non-invasive digitizer or instrumented pen signal analysis (Maarse, 1987; Baron and Plamondon, 1989; Teulings, 1996). Several studies deal with the fundamental understanding of the underlying neurocognitive and neuromotor processes (Thomassen and Teulings, 1983; Van Galen and Teulings, 1983; Van Galen, 1991) while others are primarily concerned with practical applications like on-line or off-line handwriting recognition (Nouboud and Plamondon, 1990; Plamondon et al, 1999; Plamondon and Srihari, 2000; Koerich et al, 2003; Lorigo and Govindaraju, 2006; Srihari et al, 2007; Tagougui et al, 2011), signature verification (Plamondon and Lorette, 1989; Leclerc and Plamondon, 1994; Pirlo and Impedovo, 2008; Impedovo et al, 2012; Plamondon et al, 2013a, 2014a), writer identification (Schomaker, 2007; Sreeraj and Idicula, 2011; Awaida and Mahmoud, 2012), and document analysis and processing (Doermann and Tombre, 2013). Many of these experiments rely in one way or another on a few basic models, and this is particularly true for investigations dealing with the motor control aspects of handwriting production.…”

Online Arabic writer identification based on Beta-elliptic model

“…Despite the great progress made in the field of writer identification and its applications, this area in the Arabic language remains yet an under-exploited compared to Latin or Chinese [19].…”

Toward automatic development of handwritten personal Farsi/Arabic OpenType $$^{\textregistered }$$ ® fonts

“…This prototyping process can be performed via clustering handwriting samples which we have. To cluster handwriting styles, we use contour angle histogram feature, which is used in writer identification literature [1]. This feature is computed for each writer's basic subwords.…”

“…WRIDE is a simple Matlab implementation of a system based on state-of-theart techniques for writer identification [1]. The system is based on multi-scale edge-hinge and grapheme features.…”