Semi-Supervised Online Learning of Handwritten Characters Using a Bayesian Classifier

This paper addresses the problem of creating a handwritten character recognisor, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data. We introduce an algorithm for learning from labelled and unlabelled samples based on the combination of novel online ensemble of the Randomized Naive Bayes classifiers and a novel incremental variant of the Expectation Maximization (EM) algorithm. We make use of a weighting factor to modulate the contribution of unlabelled data. An empirical evaluation of the proposed method on Tamil handwritten base character recognition proves efficacy of the proposed method to carry out incremental semi-supervised learning and producing accuracy comparable to state-of-the-art batch learning method. Online handwritten Tamil characters from the IWFHR 2006 competition dataset was used for evaluating the proposed method.