Exploring Sparse Representation for Improved Online Handwriting Recognition

This work studies the sparse representation based classification (SRC) framework for online handwriting recognition (HR) task. In this framework, first, an exemplar dictionary is created using the training samples from each of the classes in the chosen task. Subsequently, the test samples are sparse coded over exemplar dictionary for classification. In sparse coding, both l_0 - and l_1 -norm based greedy algorithms are studied. Further, for reducing the computational cost of the SRC-based HR approach, the learned exemplar dictionary has also been explored. The proposed SRC-based approach is demonstrated for character and limited vocabulary word recognition task and evaluated on three different corpora: the Assamese digit database, the UNIPEN English character database and the UNIPEN ICROW-03 English word database. The experimental results are promising over the reported works on these databases employing the hidden Markov model or the support vector machine.