Abstract
In this paper, we propose a method to generate writer dependent (WD) handwritings. We modelled the shape of character both globally and locally with probabilistic relationships between character components. Then writer independent (WI) model was trained with lots of data. Once WI model was built, the model was adapted to a training example to maximize likelihood of the example by minimization of squared error between model and instance. The experimental results of WI numeral character generation showed that global shape consistencies and variabilities of local shape were preserved. The relationships from WI model were still valid in WD models by proposed adaptation technique so that we could generate natural-looking writer specific handwritings.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
