  In this paper, we demonstrate how a generative model can be used to build a
better recognizer through the control of content and style. We are building an
online handwriting recognizer from a modest amount of training samples. By
training our controllable handwriting synthesizer on the same data, we can
synthesize handwriting with previously underrepresented content (e.g., URLs and
email addresses) and style (e.g., cursive and slanted). Moreover, we propose a
framework to analyze a recognizer that is trained with a mixture of real and
synthetic training data. We use the framework to optimize data synthesis and
demonstrate significant improvement on handwriting recognition over a model
trained on real data only. Overall, we achieve a 66% reduction in Character
Error Rate.
