By Topic

Notice of Retraction
One Radical-Based On-Line Chinese Character Recognition (OLCCR) System Using Support Vector Machine for Recognition of Radicals

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
$31 $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

5 Author(s)
Xinqiao Lv ; Sch. of Comput. Sci. & Technol., HuaZhong Univ. of Sci. & Technol., Wuhan ; Dongshan Huang ; Enming Song ; Ping Li
more authors

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

This paper proposes an approach of radical-based online handwritten Chinese character recognition using support vector machine (SVM). In our system, after the input Chinese character is preprocessed, segments ,the structure feature of the character are extracted and the middle point of each segment is projected in horizontal and vertical directions to detect that which type the character's pattern type is of single-element pattern, left-right top-bottom pattern, surrounding or half-surrounding pattern? The character is then decomposed into many meaningful sub-structures. Every substructure is a radical actually and is divided into 8 subareas for each of four directions so that the statistics feature of number of pixels in each subarea is adapted to recognize radical using SVM. Since Chinese character is composed of radicals, the recognition of Chinese character is transformed to series of radical matching between the input character and reference pattern. Furthermore, a character could have front radical and rear radical, so the front or rear radical is utilized in coarse classification stage to reduce the number of candidate characters. In respect that only radicals' feature and the radical string for a character is needed to be recorded, the template file size can be largely reduced, the speed of recognition is improved greatly. In addition, as the structure feature and the statistics feature are both adopted in this system, it is independent on stroke-number and stroke-order.

Published in:

Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on

Date of Conference:

6-8 July 2007