Discrete parameter HMM approach to on-line handwriting recognition

Abstract

One area where on-line handwriting recognition technology is most critical is the domain of small portable platforms. Because such platforms have limited resources, it is not presently practical to consider a continuous parameterization for the hidden Markov models used in the recognition. On the other hand, discrete parameter techniques such as used in speech recognition are difficult to apply, because there is no well-understood handwriting equivalent to phonological rules. A possible solution is to extract this information directly from the data, by constructing an alphabet of sub-character, elementary handwriting units. The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters.