Title :
The recognition of handwritten Chinese characters from paper records
Author :
Loudon, Gareth ; Hong, Chen ; Wu, Yi-Min ; Zitserman, Ruslana
Author_Institution :
Apple-ISS Res. Centre, Nat. Univ. of Singapore, Singapore
Abstract :
This paper describes a method used for the recognition of handwritten simplified Chinese characters from paper records. The method is based on the use of discrete hidden Markov models. The recognition accuracy achieved for all 3755 common simplified Chinese characters in the GB1 character set is 91.2% for top 1 choice and 98.5% for top 5 choice. The method recognizes isolated characters only and not words or phrases. The test set contained about 35,000 characters. All characters were written in a print style
Keywords :
hidden Markov models; image classification; optical character recognition; GB1 character set; accuracy; discrete hidden Markov models; handwritten Chinese characters; isolated characters; paper records; print style; recognition; Artificial intelligence; Artificial neural networks; Character recognition; Handwriting recognition; Hidden Markov models; Nonlinear distortion; Silicon compounds; Speech; Testing; Writing;
Conference_Titel :
TENCON '96. Proceedings., 1996 IEEE TENCON. Digital Signal Processing Applications
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-3679-8
DOI :
10.1109/TENCON.1996.608471
