Offline Character recognition on Segmented Handwritten Kannada Characters

Abstract

Optical character recognition (OCR) is the conversion of pictures of typed or handwritten characters into machine encoded characters. We chose to work on a subfield of OCR, namely offline learning of handwritten characters. Kannada script is agglutinative, where simple shapes are concatenated horizontally to form words. This paper presents a comparative study between different machine learning and deep learning models on Kannada characters. A Convolutional Neural Network (CNN) was chosen to show that handcrafted features are not required for recognizing classes to which characters belong to. The CNN beats the accuracy score of previous models by 5%. © 2019 IEEE.