18. A data recognition system, comprising: first means for determining whether one of a training process that is a prepatory step for data recognition and a recognition process for recognizing data has been activated; second means for providing a character training mode and a ligature training mode during said training process; third means for receiving first coordinate data representative of actual strokes of cursively written characters during said character training mode and for receiving second coordinate data representative of cursively written ligatures during said ligature training mode; fourth means for fitting said actual strokes of said cursively written characters with first imaginary strokes during said character training mode and for fitting said actual strokes of said cursively written ligatures with second imaginary strokes during said ligature training mode; fifth means for generating first merged strokes comprised of said actual strokes of said cursively written characters and said first imaginary strokes during said character training mode, and generating second merged strokes comprised of said actual strokes of said cursively written ligatures and said second imaginary strokes during said ligature training mode; sixth means for normalizing distances between adjacent points of said first merged strokes during said character training mode to generate first normalized strokes, and normalizing distances between adjacent points of said second merged strokes during said ligature training mode to generate second normalized strokes; seventh means for converting directional angles of vectors connecting adjacent points of said first normalized strokes into a first plurality of chain codes during said character training mode, and for converting directional angles of vectors connecting adjacent points of said second normalized strokes into a second plurality of chain codes during said ligature training mode; eighth means for recording training results by feeding said first plurality of chain codes into character hidden Markov models during said character training mode, and feeding said second plurality of chain codes into ligature hidden Markov models during said ligature training mode; ninth means for checking whether said training process is complete, and interconnecting said character hidden Markov models to said ligature hidden Markov models to construct a circularly formed hidden Markov model when said training process is complete; tenth means for activating said recognition process and receiving, for recognition, data representative of selected ones of said actual strokes of said cursively written characters and ligatures when construction of said circularly formed hidden Markov model is complete, fitting said selected ones of said actual strokes of said cursively written characters and ligatures with third imaginary strokes, generating a third merged stroke comprised of said selected ones of said actual strokes of said cursively written characters and ligatures and said third imaginary strokes, normalizing distances between adjacent points of said third merged stroke to generate a third normalized stroke and converting said third normalized stroke into a third plurality of chain codes; eleventh means for feeding said third plurality of chain codes converted by said tenth means into said circularly formed hidden Markov model; and twelfth means for providing a word represented by said selected ones of said actual strokes of said cursively written characters and ligatures, said word corresponding to an optimal path found in said circularly formed hidden Markov model.
19. The system of claim 18, further comprised of said ninth means constructing said circularly formed hidden Markov model by connecting a first node of every one of said character hidden Markov models to a global initial state and a last node of every one of said character hidden Markov models to a global final state, and further by connecting said last node of each one of said character hidden Markov models to every one of said ligature hidden Markov models and a last node of each one of said ligature hidden Markov models to said first node of every one of said character hidden Markov models.