Abstract
Nonlinear shape normalization (NSN) approaches based on line density equalization have been the most popular choice for both offline and online handwritten Chinese character recognition (HCCR). However, in a recent study of using 8-directional features for online HCCR, we discovered that an NSN approach based on dot density equalization achieved a much better performance than that of an NSN approach based on line density equalization. In this paper, we present the details of the NSN approaches we studied for online HCCR, and report the comparative experimental results using an in-house developed Chinese handwriting corpus as well as the popular Nakayosi and Kuchibue Japanese character databases. We also present an improved NSN approach based on the equalization of dot densities derived from blurred character image that can be used for offline HCCR.
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.
