Title :
Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition
Author :
Xue, Hanhong ; Govindaraju, Venu
Author_Institution :
Adv. Clustering Technol. Team, IBM, Poughkeepsie, NY, USA
fDate :
3/1/2006 12:00:00 AM
Abstract :
Prior arts in handwritten word recognition model either discrete features or continuous features, but not both. This paper combines discrete symbols and continuous attributes into structural handwriting features and model, them by transition-emitting and state-emitting hidden Markov models. The models are rigorously defined and experiments have proven their effectiveness.
Keywords :
handwritten character recognition; hidden Markov models; continuous attributes; discrete symbols; handwritten word recognition; state-emitting hidden Markov models; structural handwriting features; transition-emitting hidden Markov models; Art; Handwriting recognition; Hidden Markov models; Image recognition; Modeling; Shape; Skeleton; Stochastic processes; Vector quantization; Venus; Markov processes; handwriting analysis.; Algorithms; Artificial Intelligence; Automatic Data Processing; Documentation; Handwriting; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Markov Chains; Models, Statistical; Pattern Recognition, Automated; Reading; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2006.55
