A statistical approach with HMMs for on-line cursive Hangul (Korean script) recognition

A statistical approach to recognizing on-line cursive Hangul character is proposed. Viewing a handwritten Hangul syllable as an alternating sequence of letters and ligatures, all handwritten legal characters are modeled with a finite state network that is a concatenation of letter and ligature HMMs. Given an input to the network, recognition, which corresponds to finding the most likely path, is performed using the dynamic programming technique. Experiments have shown that letter boundary detection as well as handwriting variability resolution is achieved with good results.<<ETX>>