GMM posterior features for improving online handwriting recognition

An online handwriting recognition (HR) system is usually developed considering point-based features that describe different geometric attributes of handwriting. Often, due to the wide variations in writing styles, the use of point-based features results in high intra-class variability in feature space. To address this problem, we propose a set of features based on character class-conditional probabilities (posterior features) derived from Gaussian Mixture Model (GMM) for online HR task. The proposed features capture class dependent characteristics with a probabilistic framework, which in turn aid in minimizing the intra-class variability of the feature space. Also, the features well represent the inter-class variability of the feature space for a given classification task. The efficacy of the proposed GMM posterior features is shown for character and word recognition tasks, employing support vector machine (SVM) classifier. The experiments are conducted on three databases: the locally collected Assamese digit database, the UNIPEN English character database, and the UNIPEN ICROW-03 English word database. Recognition results are promising over the reported works employing point-based features.