1. A computer-based method for recognizing handwriting, wherein the computer comprises an input device, a memory module, a preprocessor unit, a front end unit and a modeling component, the method comprising the steps of: (1) receiving character signals into the preprocessor unit from the input device, said character signals representing training observation sequences of sample characters; (2) sorting said character signals in the preprocessor unit according to lexemes which represent different writing styles for a given character, by mapping said character signals in lexographic space, said lexographic space being a location in the memory module which contains one or more character-level feature vectors, to find high-level variations in said character signals; (3) selecting one of said lexemes; (4) generating sequences of feature vector signals in the front end unit representing feature vectors for said character signals associated with said selected lexeme by mapping in chirographic space, said chirographic space being a location in the memory module which contains one or more flame-level feature vectors; and (5) generating a Markov model signal in the modeling component representing a hidden Markov model for said selected lexeme, said hidden Markov model having model parameter signals and one or more states, each of said states having emission transitions and non-emission transitions, wherein said step (5) comprises the steps of: (i) initializing said model parameter signals comprising the steps of: (a) setting a length for said hidden Markov model; (b) initializing state transition probabilities of said hidden Markov model to be uniform; (c) for each of said states, tying one or more output probability distributions for said emission transitions; (d) for each of said states, assigning a Gaussian density distribution for each of one or more codebooks; and (e) alternatively initializing one or more mixture coefficients to be values obtained from a statistical mixture model; and (ii) updating said model parameter signals.