Offline Kannada Handwritten Character Recognition Using Convolutional Neural Networks

Abstract

Handwritten characters are still far from being replaced with the digital form. The occurrence of handwritten text is abundant. With a wide scope, the problem of handwritten letter recognition using computer vision and machine learning techniques has been a well pondered upon topic. The field has undergone phenomenal development, since the emergence of machine learning techniques. This work on a major scale devises to bridge the gap between the state-of-the-art technology, of deep learning, to automate the solution to handwritten character recognition, using convolutional neural networks. Convolutional neural networks have been known to have performed extremely well, on the vintage classification problem in the field of computer vision. Using the advantages of the architecture and leveraging on the preprocessing free deep learning techniques, we present a robust, dynamic and swift method to solve the problem of handwritten character recognition, for Kannada language. We discuss the performance of the network on two different approaches with the dataset. The obtained accuracy measured upto 93.2 % and 78.73 % for the two different types of datasets used in the work. The results obtained have been plotted and presented in the relevant sections.