1. A method for performing handwriting recognition on a received data set, said received data set comprising data points representative of a handwriting sample, said method comprising the steps of: preprocessing said data points of said data set and generating a set of resampled data points, said resampled data points being substantially equidistantly spaced along said handwriting sample, said preprocessing step further comprising the step of preprocessing said data points of said data set to reduce signal abnormalities of said data set; said preprocessing step further including the step of filtering said data set to remove extraneous noise, wherein said filtering step further includes the steps of: identifying cusps within said data set, said identifying cusps step further including the steps of: constructing a focus region, said focus region including data points in the data set in sequence starting from a starting focus point to an ending focus point, said starting focus point corresponding to the first data point in the data set, said ending focus data point being at least two data points in sequence from said starting focus point; determining linear distances to the starting focus point and the ending focus point for each said data point in the focus region; identifying a data point in the focus region as a potential cusp point if the magnitude of either of the starting or the ending focus point linear distances exceeds the linear distance between the starting and the ending focus point; determining, for each of the potential cusp points, the smaller of the linear distance from the potential cusp data point to the starting focus point and the linear distance from the potential cusp point to the ending focus point; determining whether the maximum of the smaller linear distances for all the potential cusp points exceeds a threshold value; and, storing the data point corresponding to the maximum as a cusp point; and screening said extraneous noise from said data set utilizing a curve approximating technique, said curve approximating technique treating each identified cusp as a boundary point, said preprocessing step further including the step of normalizing said data set to reduce the geometric variance in said handwriting sample thereby causing said data set to lie within a prescribed range; computing a sequence of features from said resampled data points; comparing each feature to stochastic recognition models to obtain feature scores, said stochastic recognition models comprising a plurality of probability distributions; and propagating said feature scores in sequence through an evolutional grammar network for obtaining cumulative hypothesis scores using a stochastic recognition algorithm.