By Topic

A floating feature detector for handwritten numeral recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zhang Ping ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; Chen Lihui ; A. C. Kot

A novel feature extraction method for handwritten numeral recognition is proposed based on character's geometric structures. A group of stable and reliable global features are defined and extracted. Furthermore, a floating feature detector is proposed to detect and extract tiny segments as fine features. A neural network is employed as the recognisor to conduct experiments on evaluating the feasibility of the new approach. This proposed method demonstrates that the combination of fine features with global features can greatly improve the handwritten character recognition rate compared to those using global features only

Published in:

Pattern Recognition, 2000. Proceedings. 15th International Conference on  (Volume:2 )

Date of Conference: