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
fDate :
5/1/1996 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on