The Community for Technology Leaders
Ninth International Workshop on Frontiers in Handwriting Recognition (2004)
Kokubunji, Tokyo, Japan
Oct. 26, 2004 to Oct. 29, 2004
ISSN: 1550-5235
ISBN: 0-7695-2187-8
pp: 521-526
Hidetoshi Miyao , Shinshu University
Youichi Mitobe , TOSYS Corporation
Minoru Maruyama , Shinshu University
The Hidden Markov Model (HMM) has been successfully applied to various kinds of on-line recognition problems including, speech recognition, handwritten character recognition, etc. In this paper, we propose an on-line method to recognize handwritten music scores. To speed up the recognition process and improve usability of the system, the following methods are explained: (1) The target HMMs are restricted based on the length of a handwritten stroke, and (2) Probability calculations of HMMs are successively made as a stroke is being written. As a result, recognition rates of 85.78% and average recognition times of 5.19ms/stroke were obtained for 6,999 test strokes of handwritten music symbols, respectively. The proposed HMM recognition rate is 2.4% higher than that achieved with the traditional method, and the processing time was 73% of that required by the traditional method.
HMM, Handwritten Music Score Recognition, On-line Symbol Recognition
Hidetoshi Miyao, Youichi Mitobe, Minoru Maruyama, "A Fast HMM Algorithm Based on Stroke Lengths for On-Line Recognition of Handwritten Music Scores", Ninth International Workshop on Frontiers in Handwriting Recognition, vol. 00, no. , pp. 521-526, 2004, doi:10.1109/IWFHR.2004.2
105 ms
(Ver 3.1 (10032016))