Notice of Violation of IEEE Publication PrinciplesAn Improved Online Tamil Character Recognition Using Neural Networks

Notice of Violation of IEEE Publication Principles

"An Improved Online Tamil Character Recognition Using Neural Networks"
by M.V. Ishwarya, R. Jagadeesh Kannan
in the Proceedings of the International Conference on Advances in Computer Engineering, June 2010, pp. 284-288

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 is a duplication of the original text from the papers cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper titles) 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 articles:

"An Improved Online Tamil Character Recognition Engine using Post-Processing Methods"
by Suresh Sundaram, A.G. Ramakrishnan
in the Proceedings of the 10th International Conference on Document Analysis and Recognition July 2009, pp. 1216-1220

"Character Recognition Using Convolutional Neural Networks"
by David Bouchain
in Seminar Statistical Learning Theory, 2006/2007

In this paper we propose a Conventional Neural Network which recognizes the online Tamil characters. Convolutional Neural Networks are a special kind of multi-layer neural networks. Like almost every other neural networks they are trained with a version of the back-propagation algorithm, where they differ is in the architecture. Convolutional Neural Networks are designed to recognize visual patterns directly from pixel images with minimal preprocessing. They can recognize patterns with extreme variability (such as handwritten characters), and with robustness to distortions and geometric transformations. The existing script-specific post processing schemes improved the re- ognition rate of online Tamil characters. At the first level, features derived at each sample point of the preprocessed character are used to construct a subspace using the 2DPCA algorithm. Recognition of the test sample is performed using a nearest neighbor classifier. This strategy reduces the recognition error among the confused character sets handled, by more than 4%.