a new learning-based spatiotemporal descriptor for online symbol recognition

The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. moreover, most recognition studies utilize local features or sequences of them. whereas, it has been shown that the combination of global and local features can increase the recognition accuracy. this paper addresses two mentioned topics. first, a new high discriminative local feature, called rotation invariant histogram of degrees (rihod), is proposed for online digitizer-pen handwriting signals. second, a feature representation layer is proposed, which maps local features into global ones in a new space using some learning kernels. different aspects of the proposed local feature and learned global feature are analyzed and its efficiency is evaluated in several online handwriting recognition scenarios.