Title :
A statistical approach with HMMs for on-line cursive Hangul (Korean script) recognition
Abstract :
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
Keywords :
character recognition; dynamic programming; finite automata; handwriting recognition; hidden Markov models; HMMs; Korean script; alternating sequence; dynamic programming technique; finite state network; handwriting variability resolution; handwritten Hangul syllable; handwritten legal characters; letter boundary detection; letters; ligatures; most likely path; on-line cursive Hangul character; statistical approach; Hidden Markov models; Silicon compounds; Stochastic processes; Topology; Writing;
Conference_Titel :
Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on
Conference_Location :
Tsukuba Science City
Print_ISBN :
0-8186-4960-7
DOI :
10.1109/ICDAR.1993.395762
