1. A method of recognizing handwriting, comprising using one or more processors to perform the following computer-executable acts: receiving a first sample of a user's handwriting in electronic ink; comparing the first sample to samples in a candidate sample data store; if no match is found for the first sample in the candidate sample data store, storing the first sample at least temporarily in the candidate sample data store; receiving a second sample of the user's handwriting in electronic ink; comparing the second sample with the first sample in the candidate sample data store; if a distance between the first sample and the second sample is below a threshold, adding at least one of the first and second samples to a user prototype data store; receiving a handwriting input of the user; employing the user prototype data store to produce first recognition results from the handwriting input; employing a generic recognition engine to produce second recognition results from the handwriting input; providing the handwriting input, the first recognition results and the second recognition results to a combiner, wherein the combiner is a comparative neural network that has been trained to distinguish a better match between the first and second recognition results and the handwriting input; and combining, utilizing the combiner, the first recognition results and the second recognition results to perform handwriting recognition, wherein the comparative neural network compares the first recognition results and the second recognition results and chooses the one that better matches the input handwriting, based on having been trained.
4. The method recited in claim 1 , wherein the first sample is employed to identify a generic handwriting prototype to be used by a prototype-based handwriting recognition engine.