A Semi-supervised SVM Framework for Character Recognition

In order to incorporate various writing styles or fonts in a character recognizer, it is critical that a large amount of labeled data is available, which is difficult to obtain. In this work, we present a semi-supervised SVM based framework that can incorporate the unlabeled data for improvement of recognition performance. Existing semi supervised learning methods for SVMs work well only for two-class problems. We propose a method to extend this to large-class problems by incorporating a participation term into the optimization process. The proposed system uses a Decision Directed Acyclic Graphs (DDAG) of SVM classifiers, which have proven to be very effective for such recognition problems. We present experimental results on three different digits dataset with varying complexity, as well as additional multi-class datasets from the UCI repository for comparison with existing approaches. In addition we show that approximate annotations at the word or sentence level can be used for evaluation as well as active learning to further improve the recognition results.