DocumentCode :
900707
Title :
Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques
Author :
Mohamed, Magdi ; Gader, Paul
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA
Volume :
18
Issue :
5
fYear :
1996
fDate :
5/1/1996 12:00:00 AM
Firstpage :
548
Lastpage :
554
Abstract :
A lexicon-based, handwritten word recognition system combining segmentation-free and segmentation-based techniques is described. The segmentation-free technique constructs a continuous density hidden Markov model for each lexicon string. The segmentation-based technique uses dynamic programming to match word images and strings. The combination module uses differences in classifier capabilities to achieve significantly better performance
Keywords :
character recognition; computer vision; dynamic programming; hidden Markov models; image matching; image segmentation; learning systems; neural nets; dynamic programming; handwritten word recognition; hidden Markov modeling; image classifier; image matching; lexicon string; neural networks; segmentation; Algorithm design and analysis; Character recognition; Design engineering; Fuzzy logic; Handwriting recognition; Hidden Markov models; Image segmentation; Neural networks; Shape; System testing;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.494644
Filename :
494644
Link To Document :
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