Notice of Violation of IEEE Publication PrinciplesA New Approach For Rotation Invariant Optical Character Recognition Using Eigendigit

Notice of Violation of IEEE Publication Principles

"A New Approach For Rotation Invariant Optical Character Recognition Using Eigendigit"
by Md. Maruf Monwar, Waqar Haque, Padma Polash Paul
in the Canadian Conference on Electrical and Computer Engineering (CCECE 2007), 2007, pp. 1317 - 1320

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

This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Eigenfaces for Recognition"
by Matthew Turk, Alex Pentland
in the Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 71 - 86

"A New Approach to Bangla Text Extraction and Recognition From Textual Image"
by Md. Al Mehedi Hasan, Md. Abdul Alim, Md. Wahedul Islam
in 8th International Conference on Computer and Information Technology (ICCIT 2005), 2005, pp. 1 - 5

"Eigenface Tutorial"
By Drexel University
available at http://www.pages.drexel.edu/~sis26/Eigenface%20Tutorial.htm

Character recognition plays an important role in the modern world. It makes human\´s job easier when solving more complex problems. We have developed and implemented a rotation invariant character recognition technique by extending the eigenface method previously presented in the literature. Our proposed approach treats each character as a two-dimensional recognition problem, taking advantage of the fact that characters can be described by a small set of 2D characteristic views of different angles (0- eg-360deg). Character images of different angles are projected onto a feature space ("eigen space") that best encodes the variation among known character images. The proposed system is based on principal component analysis which makes it efficient in learning and recognizing characters of different angle.