Statistical Deformation Model for Handwritten Character Recognition

Abstract

One of the main problems of offline and online handwritten character recognition is how to deal with the deformations in characters. A promising strategy to this problem is the incorporation of a deformation model. If recognition can be done with a reasonable deformation model, it may become tolerant to deformations within each character category. There have been proposed many deformation models and some of them were designed in an empirical manner. Recognition methods based on elastic matching have often relied on a continuous and monotonic deformation model (Bahlmann & Burkhardt, 2004; Burr, 1983; Connell & Jain, 2001; Fujimoto et al., 1976; Yoshida & Sakoe, 1982). This is a typical empirical model and has been developed according to the observation that character patterns often preserve their topologies. Affine deformation models (Wakahara, 1994; Wakahara & Odaka, 1997; Wakahara et al., 2001) and local perturbation models (or image distortion models (Keysers et al., 2004)) are also popular empirical deformation models. While the empirical models generally work well in handwritten character recognition tasks, they are not well-grounded by actual deformations of handwritten characters. In addition, the empirical models are just approximations of actual deformations and they cannot incorporate category-dependent deformation characteristics. In fact, the category-dependent deformation characteristics exist. For example, in category “M”, two parallel vertical strokes are often slanted to be closer. In contrast, in category “H”, however, the same deformation is rarely observed. Statistical models are better alternatives to the empirical models. The statistical models learn deformation characteristics from actual character patterns. Thus, if a model learns the deformations of a certain category, it can represent the category-dependent deformation characteristics. Hidden Markov model (HMM) is a popular statistical model for handwritten characters (e.g., (Cho et al., 1995; Hu et al., 1996; Kuo & Agazzi, 1994; Nag et al., 1986; Nakai et al., 2001; Park & Lee, 1998)). HMM has not only a solid stochastic background and but also a well-established learning scheme. HMM, however, has a limitation on regulating global deformation characteristics; that is, HMM can regulate local deformations of neighboring regions due to its Markovian property. This chapter is concerned with another statistical deformation model of offline and online handwritten characters. This deformationmodel is based on a combination of elasticmatching and principal component analysis (PCA) and also capable of learning actual deformations of 1