23. The method as claimed in claim 20, wherein said step of interconnecting said character Markov models and said ligature Markov models comprises the steps of: connecting a first node of each one of said character Markov models to a global initial state with a null-arc and a last node of each one of said character Markov models to a final state with said null-arc, said connection step being sequentially performed for every one of said character Markov models in said circular hidden Markov model; and connecting said last node of each one of said character Markov models to a first node of every one of said ligature Markov models and a last node of each one of said ligature Markov models to said first node of every one of said character Markov models 21, further comprised of said actual strokes representative of said characters and ligatures being input on a variable visual display by a user via an electrical pen capable of altering said variable visual display.
20. A method for training a data recognition system to recognize words comprised of characters and ligatures connecting said characters, said method comprising the steps of: inputting coordinates of actual strokes representative of said characters and ligatures; fitting imaginary strokes between said actual strokes to generate merged strokes comprised of said actual strokes and said imaginary strokes; normalizing distances between adjacent points of said merged strokes to generate normalized strokes; converting directional angles of vectors connecting adjacent points of said normalized strokes into a plurality of chain codes; recording training results by incorporating said plurality of chain codes into character Markov models and ligature Markov models; and interconnecting said character Markov models and said ligature Markov models to generate a circularly formed hidden Markov model for enabling recognition of said words comprised of said characters and ligatures, said circularly formed hidden Markov model being generated by connecting a first node of every one of said character Markov models to a global initial state and a last node of every one of said character Markov models to a global final state and connecting said last node of each one of said character Markov models to a first node of every one of said ligature Markov models and a last node of each one of said ligature Markov models to said first node of every one of said character Markov models.