  Increased accuracy in predictive models for handwritten character recognition
will open up new frontiers for optical character recognition. Major drawbacks
of predictive machine learning models are headed by the elongated training time
taken by some models, and the requirement that training and test data be in the
same feature space and consist of the same distribution. In this study, these
obstacles are minimized by presenting a model for transferring knowledge from
one task to another. This model is presented for the recognition of handwritten
numerals in Indic languages. The model utilizes convolutional neural networks
with backpropagation for error reduction and dropout for data overfitting. The
output performance of the proposed neural network is shown to have closely
matched other state-of-the-art methods using only a fraction of time used by
the state-of-the-arts.
