In this paper we present a new method for a visual cryptography scheme that uses random basis column pixel expansion technique. This method encoded the secret image into 'n' share images in a recursive manner, which have dimensions identical to the secret image. In this scheme, the encoded n shares further encoded into number of subshares recursively. It will increase the level of security of the encrypted message. 10th International Conference on Information Technology 0-7695-3068-0/07 $25.00 © 2007 IEEE DOI 41 10th International Conference on Information Technology 0-7695-3068-0/07 $25.00

Ahstract-The machine recognition of handwriting has been stu died several decades, and still is an active research area due to the presence of many challenging issues. Several measures have been taken to improve the recognition accuracy and decrease computational burden and time involved in the overall recogni tion process. This work is carried out in this context, and tried to achieve the above goals. A discriminative feature set is an impor tant component in any pattern recognition system. We have used division point features generated by recursive subdivision of cha racter images. These features are extracted from a size norma lized binary image.In many of the applications data arrive chunk by chunk or one by one, and we need a classifier which handles the data sequentially. In this paper, a fast and accurate online sequential algorithm known as OS-ELM is used for single hidden layer feed forward neural networks (SLFN) with both additive and radial basis function (RBF) hidden nodes in a uni fied way. We have also considered the fact that the complexity of the system increases as the number of hidden layers increases.An SLFN trained with OS-ELM is used for the classification of 44 class handwritten Malayalam characters. The only control parameter required for this system is the size of the hidden layer.After several practices we have achieved a recognition accuracy of 96.83% with 1200 neurons in the hidden layer.

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