Generation of Handwriting by Active Shape Modeling and Global Local Approximation (GLA) Adaptation

Abstract

The generation of handwriting is a complex task. In order to accommodate for the large variations involved in handwritten words deformable templates need to be used. In this paper we propose a handwriting model, based on Active shape modeling (ASM). In a two-step generation process, a template-based ASM generates characters and a Gaussian mixture regression (GMR) model concatenates the generated characters. For real time generation of cursive handwriting an adaptation of Global local approximation (GLA) methodology is used to fit the generated models.