6. A method of sequentially receiving on-line input data representative of actual strokes of characters and ligatures connecting said characters and recognizing a word represented by said characters wherein said input data is represented as sequences of coordinates, said method comprising the steps of: receiving and storing said input data representative of said actual strokes; identifying any severed portions existing between said actual strokes; inserting an imaginary stroke at each one of said severed portions; merging, as one stroke, said actual strokes and each said imaginary stroke to form a merged stroke; normalizing distances between adjacent coordinates in said merged stroke to generate a normalized stroke; converting data representative of said normalized stroke into a plurality of chain codes; finding an optimal path in a circularly formed hidden Markov model comprised of interconnected character and ligature models, said circularly formed hidden Markov model constructed in a training mode by connecting a first node of every one of said character models to a global initial state and a last node of every one of said character models to a global final state, and connecting said last node of each one of said character models to a first node of every one of said ligature models and a last node of each one of said ligature models to said first node of every one of said character models; and recognizing said word represented by said characters, including both discrete style and cursive style characters, in dependence upon said optimal path.
6. A method of sequentially receiving on-line input data representative of actual strokes of characters and ligatures connecting said characters and recognizing a word represented by said characters wherein said input data is represented as sequences of coordinates, said method comprising the steps of: receiving and storing said input data representative of said actual strokes; identifying any severed portions existing between said actual strokes; inserting an imaginary stroke at each one of said severed portions; merging, as one stroke, said actual strokes and each said imaginary stroke to form a merged stroke; normalizing distances between adjacent coordinates in said merged stroke to generate a normalized stroke; converting data representative of said normalized stroke into a plurality of chain codes; finding an optimal path in a circularly formed hidden Markov model comprised of interconnected character and ligature models, said circularly formed hidden Markov model constructed in a training mode by connecting a first node of every one of said character models to a global initial state and a last node of every one of said character models to a global final state, and connecting said last node of each one of said character models to a first node of every one of said ligature models and a last node of each one of said ligature models to said first node of every one of said character models; and recognizing said word represented by said characters, including both discrete style and cursive style characters, in dependence upon said optimal path. 7. The method as claimed in claim 6, wherein said imaginary stroke is inserted at each one of said severed portions to connect an ending point of a first one of said actual strokes with a starting point of a second one of said actual strokes, said second one of said actual strokes being entered by a user immediately following said first one of said actual strokes.