1. A method of sequentially receiving on-line input data as sequences of coordinates and recognizing a word represented by said input data in a data recognition system, said method comprising the steps of: training said system to recognize individual character units by entering said character units into said system, fitting said character units with first corresponding imaginary strokes, preprocessing said character units, converting said character units into corresponding sequences of first chain codes and representing said first chain codes in first hidden Markov models; training said system to recognize ligatures connecting said character units by entering said ligatures into said system, fitting said ligatures with second corresponding imaginary strokes, preprocessing said ligatures, converting said ligatures into corresponding sequences of second chain codes and representing said second chain codes in second hidden Markov models; interconnecting said first hidden Markov models with said second hidden Markov models to form a circularly connected hidden Markov model by connecting a first node of every one of said first hidden Markov models to a global initial state and a last node of every one of said first hidden Markov models to a global final state, and connecting said last node of each one of said first hidden Markov models to a first node of every one of said second hidden Markov models and a last node of each one of said second hidden Markov models to said first node of every one of said first hidden Markov models; and recognizing said word represented by said input data by entering said word into said system, fitting said word with third corresponding imaginary strokes, preprocessing said word, converting said word into corresponding sequences of third chain codes and passing said third chain codes through said circularly connected hidden Markov model.
1. A method of sequentially receiving on-line input data as sequences of coordinates and recognizing a word represented by said input data in a data recognition system, said method comprising the steps of: training said system to recognize individual character units by entering said character units into said system, fitting said character units with first corresponding imaginary strokes, preprocessing said character units, converting said character units into corresponding sequences of first chain codes and representing said first chain codes in first hidden Markov models; training said system to recognize ligatures connecting said character units by entering said ligatures into said system, fitting said ligatures with second corresponding imaginary strokes, preprocessing said ligatures, converting said ligatures into corresponding sequences of second chain codes and representing said second chain codes in second hidden Markov models; interconnecting said first hidden Markov models with said second hidden Markov models to form a circularly connected hidden Markov model by connecting a first node of every one of said first hidden Markov models to a global initial state and a last node of every one of said first hidden Markov models to a global final state, and connecting said last node of each one of said first hidden Markov models to a first node of every one of said second hidden Markov models and a last node of each one of said second hidden Markov models to said first node of every one of said first hidden Markov models; and recognizing said word represented by said input data by entering said word into said system, fitting said word with third corresponding imaginary strokes, preprocessing said word, converting said word into corresponding sequences of third chain codes and passing said third chain codes through said circularly connected hidden Markov model. 4. The method as claimed in claim 1, wherein in said step of recognizing said word, an optimal path in said circularly connected hidden Markov model providing a highest probability of occurrence is selected based on said third chain codes.