Previous work on handwriting recognition has mainly focused on processing images of pen-on-paper writing, i.e. offline character recognition. Notably, the MNIST dataset was created using images of handwritten US census returns [LeCun et al., 1998]. Excellent recognition accuracy (99.2%) was demonstrated on the MNIST dataset using a convolutional neural network (ConvNet) [LeCun et al., 1998]. In contrast, online character recognition systems take input in the form of the continuously sensed position of the pen, finger, or thumb. Online systems have the advantage of recording temporal information as well as spatial information. To date, most work on online character recognition has focused on pen based systems [Guyon et al., 1991, Bengio et al., 1995, Verma et al., 2004, Bahlmann, 2006]. LeCun et al.’s paper proposed a ConvNet approach to the problem, achieving 96% accuracy. The method involved considerable preprocessing without which accuracy falls to 60%. The preprocessing step requires that the entire glyph is known a priori, removing the possibility of early recognition and completion of the glyph.