Dataset Building for Handwritten Kannada Vowels Using Unsupervised and Supervised Learning Methods

In the era of automation, recognition of the Kannada handwritten characters is an inevitable task as it has widespread applications in the digitization of documents in government offices, public sectors, and other domains like banking and post offices. Hence, the need for Optical Character Recognizers (OCR) for the Indian languages like Kannada is vital. This paper presents the recognition and labeling of offline Handwritten Kannada Vowels using the feature extraction techniques like Local Binary Pattern (LBP), Run Length Count (RLC), Chain Code (CC) and Histogram of Oriented Gradients (HOG) and feeding the features to the supervised and unsupervised machine learning algorithms with pure and hybrid features is presented. The comparative study of supervised learning algorithms on the data collected from 500 people. Also, the objective of this work is to label the unlabeled data by automation without manual labor. This has been achieved by feeding the features initially to the unsupervised learning algorithm, i.e., KMeans Clustering algorithm. The classified and misclassified vowels, then became the train and test sets respectively for supervised learning algorithms and a combined recognition rate has been presented.