DocumentCode :
311126
Title :
A comparison of discrete and continuous hidden Markov models for phrase spotting in text images
Author :
Chen, Francine R. ; Wilcox, Lynn D. ; Bloomberg, Dan S.
Author_Institution :
Xerox Palo Alto Res. Center, CA, USA
Volume :
1
fYear :
1995
fDate :
14-16 Aug 1995
Firstpage :
398
Abstract :
In spotting for phrases in text images, speed and accuracy are important considerations. In a hidden Markov model (HMM) based spotter recognition time is dominated by the time required to compute the state conditional observation probabilities. These probabilities are a measure of how well the data match each state in the model. In this paper discrete and continuous hidden Markov models are compared based on speed and accuracy in spotting for phrases in text images. For the discrete HMM, vector quantization is used to associate each continuous feature vector with a discrete value. For the continuous HMMs, the observation distributions for the feature vectors are modeled by either a single Gaussian, or a mixture of two Gaussians. Comparisons were made on a subset of the UW English Document Image Database I. The best accuracy was observed when a mixture of two Gaussians was used in the continuous HMM. The discrete HMM provides for faster spotting particularly when long phrases are used
Keywords :
hidden Markov models; image recognition; optical character recognition; vector quantisation; continuous feature vector; feature vectors; hidden Markov models; observation distributions; phrase spotting; state conditional observation probabilities; text images; vector quantization; Character recognition; Handwriting recognition; Hidden Markov models; Image databases; Image recognition; Image segmentation; Robustness; Speech recognition; Text recognition; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-8186-7128-9
Type :
conf
DOI :
10.1109/ICDAR.1995.599022
Filename :
599022
Link To Document :
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