17. A computer-based system for recognizing handwriting, the computer comprising an input device, a memory module, a preprocessor unit, a front end unit and a modeling component, the system comprising: (a) means for receiving character signals into the preprocessor unit from the input device representing training observation sequences of sample characters; (b) means for sorting said character signals in the preprocessor unit according to lexemes which represent different writing styles for a given character, by mapping said sample characters 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; (c) means for generating sequences of feature vector signals in the front end unit representing feature vectors for said character signals by mapping in chirographic space, said chirographic space being a location in the memory module which contains one or more frame-level feature vectors; (d) means for generating Markov model signals in the modeling component representing hidden Markov models for said lexemes, each of said hidden Markov models having model parameter signals and one or more states, each of said states having emission transitions and non-emission transitions, wherein said generating means comprises: (i) means for initializing said model parameter signals in each of said hidden Markov models, said initializing means comprising: means for setting a length for said hidden Markov model; means for initializing state transition probabilities of said hidden Markov model to be uniform; means for tying one or more output probability distributions for said emission transitions for each of said states; means for assigning a Gaussian density distribution for each of one or more codebooks for each of said states; and means for alternatively initializing one or more mixture coefficients to be values-obtained from a statistical mixture model; and (ii) means for updating said model parameter signals in each of said hidden Markov models.
17. A computer-based system for recognizing handwriting, the computer comprising an input device, a memory module, a preprocessor unit, a front end unit and a modeling component, the system comprising: (a) means for receiving character signals into the preprocessor unit from the input device representing training observation sequences of sample characters; (b) means for sorting said character signals in the preprocessor unit according to lexemes which represent different writing styles for a given character, by mapping said sample characters 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; (c) means for generating sequences of feature vector signals in the front end unit representing feature vectors for said character signals by mapping in chirographic space, said chirographic space being a location in the memory module which contains one or more frame-level feature vectors; (d) means for generating Markov model signals in the modeling component representing hidden Markov models for said lexemes, each of said hidden Markov models having model parameter signals and one or more states, each of said states having emission transitions and non-emission transitions, wherein said generating means comprises: (i) means for initializing said model parameter signals in each of said hidden Markov models, said initializing means comprising: means for setting a length for said hidden Markov model; means for initializing state transition probabilities of said hidden Markov model to be uniform; means for tying one or more output probability distributions for said emission transitions for each of said states; means for assigning a Gaussian density distribution for each of one or more codebooks for each of said states; and means for alternatively initializing one or more mixture coefficients to be values-obtained from a statistical mixture model; and (ii) means for updating said model parameter signals in each of said hidden Markov models. 18. The system of claim 17, further comprising: (e) means for receiving test characters; (f) means for generating sequences of feature vectors for said test characters by mapping in chirographic space; (g) means for computing probabilities that said test characters can be generated by said hidden Markov models; and (h) means for decoding said test characters as recognized characters associated with said selected hidden Markov models having greatest probabilities.