Online sequential extreme learning machine based handwritten character recognition

The machine recognition of handwriting has been studied 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 recognition process. This work is carried out in this context, and tried to achieve the above goals. A discriminative feature set is an important component in any pattern recognition system. We have used division point features generated by recursive subdivision of chamaracter images. These features are extracted from a size normalized 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 unified 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.