Writing on Clouds

Abstract

While writer-independent handwriting recognition systems are now achieving good recognition rates, writer-dependent systems will always do better. We expect this difference in performance to be even larger for certain applications, such as mathematical handwriting recognition, with large symbol sets, symbols that are often poorly written, and no fixed dictionary. In the past, to use writer-dependent recognition software, a writer would train the system on a particular computing device without too much inconvenience. Today, however, each user will typically have multiple devices used in different settings, or even simultaneously. We present an architecture to share training data among devices and, as a side benefit, to collect writer corrections over time to improve personal writing recognition. This is done with the aid of a handwriting profile server to which various handwriting applications connect, reference, and update. The user’s handwriting profile consists of a cloud of sample points, each representing one character in a functional basis. This provides compact storage on the server, rapid recognition on the client, and support for handwriting neatening. This work uses the word “cloud” in two senses. First, it is used in the sense of cloud storage for information to be shared across several devices. Secondly, it is used to mean clouds of handwriting sample points in the function space representing curve traces. We “write on clouds” in both these senses.