Multi-Class Kannada Character Recognition Using Machine Learning Methods
Kusumika Krori Dutta, Premila Manohar, S. Poornima, Ayush Renith, Chirag Vasist · 2022
Language plays a significant role in the identity of a person, it expresses history and culture. But with increasing popularity of cosmopolitan culture, the new generation is moving away from their origin. Karnataka is one of the most popular states in India which welcomes people from different geographical locations, because of its hospitality, weather, technological forefront etc. On the other hand, it impacts the usage of Kannada language and it challenges the demographic identity of the place. This paper aims to enhance the usage of Kannada language by automatic handwritten Kannada character recognition. In this work MSRIT Kannada handwritten dataset is used to classify 603 characters, which includes consonants, vowels, numbers, ottaksharas and consonants with vowels, using Decision Tree (DT), Convolutional Neural networks (CNN), Support Vector Classifier (SVC). The machine learning algorithms are modeled in python and achieved test accuracy of 97.42% using the decision tree method.