9. The processing system as set forth in claim 8 wherein said processing unit concatenates said recognized data subsets together by chaining stroke scores together which exceed said threshold level, and determining said chain of stroke scores which represents said symbols of said handwriting sample.
8. A processing system for performing handwriting analysis, said processing system comprising: an input port operable to receive a data set representative of a handwriting sample, said handwriting sample comprising one or more symbols; a memory storage device operable to store a plurality of processing system instructions; a processing unit for analyzing said data set by retrieving and executing at least one of said processing unit instructions from said memory storage device, said processing unit operable to: preprocess said data points of said data set and generate a set of resampled data points, said resampled data points being substantially equidistantly spaced along said handwriting sample; compute a sequence of features from said resampled data points; compare each feature to stochastic recognition models to obtain feature scores, said stochastic recognition models comprising a plurality of probability distributions; and propagate said feature scores in sequence through an evolutional grammar network for obtaining cumulative hypothesis scores using a stochastic recognition algorithm, wherein said evolutional grammar network is initially null, and said processing unit is further operable to designate an initial segment score to a grammar start state; said processing unit further operable to: dynamically create stroke representations from said grammar start state for each of said strokes; maintain a score for each of said stroke representations created; propagate stroke scores meeting a threshold level; and update said stroke scores to maintain only active stroke representations having stroke scores above said threshold level.