Exploring Deep Learning Techniques for Kannada Handwritten Character Recognition: A Boon for Digitization

Abstract

Kannada handwritten reports were the only way of documentation available in government offices and healthcare departments in Karnataka state. Reproducing the contents of these old documents through typewriting is a tedious task, as the documents are difficult to read and understand. Hence there is a need for a computer-based system to overcome the gap between machines and humans. The paper proposes an efficient method for Kannada handwritten character recognition system which uses image preprocessing techniques to enhance the quality of an image and exploring deep learning technique for feature extraction. The layout of the proposed method is kept simple and easy to understand for a user. Chars74K dataset was used for experimentation of the work. Experiments were performed on handwritten Kannada vowels and consonants consisting of 25 handwritten characters in 657 classes. To validate the model, Categorical Cross-entropy loss function was used with 15 epochs to measure the error rate. The model gave a prediction performance of 95.11% for the training set and 86 % for the testing set. The proposed model would be highly useful in government sectors for documentation purpose.

Keywords: Image Preprocessing, Convolution Neural Network, Artificial Neural Network, Handwritten Recognition, Kannada Characters.