DocumentCode :
1742865
Title :
Training of hidden Markov models for cursive handwritten word recognition
Author :
Bojovic, Marija ; Savic, Milan D.
Author_Institution :
KPN Res., Yugoslavia
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
973
Abstract :
We present a comparison of performances of systems for recognition of handwritten cursive words based on discrete and semi-continuous HMMs. We used lexicon and concatenation of character HMMs to generate word HMM that is matched with input word image. Character models are trained on characters written isolated with simple 16-dimensional low resolution bitmap features. This kind of features enables good visual inspection of the quantization result. Results are given for lexicon of 40 Cyrillic lowercase words. The best recognition rate of 91.5% is achieved with discrete model and PDFs with global distribution parameters. The same system using the 3 best hypotheses gives the recognition rate of 96.7%
Keywords :
feature extraction; handwritten character recognition; hidden Markov models; image coding; learning systems; vector quantisation; bitmap features; feature extraction; handwritten character recognition; handwritten cursive words; hidden Markov models; lexicon; vector quantisation; Character generation; Character recognition; Feature extraction; Handwriting recognition; Hidden Markov models; Impedance matching; Inspection; Stochastic processes; Training data; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
ISSN :
1051-4651
Print_ISBN :
0-7695-0750-6
Type :
conf
DOI :
10.1109/ICPR.2000.905624
Filename :
905624
Link To Document :
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