Paper Info
Title
A comparative study between decision fusion and data fusion in Markovian printed character recognition
Abstract
A comparison is made between several hidden Markov models in the context of printed character recognition. Two HMMs are first compared, one dealing with columns of a character image, and the other dealing with lines. These 2 HMMs are then associated in a decision fusion scheme combining the log-likelihoods provided by each HMM classifier. The statistical assumptions underlying the combination formula are described and the combination formula is shown to be an approximation of a real joint log-likelihood. The last experiment consists of building a single HMM, modeling the joint flow of lines and columns. This data fusion scheme is shown to be more accurate as it highlights correlations between the line and column features.
Year
DOI
Venue
2002
10.1109/ICPR.2002.1047816
Pattern Recognition, 2002. Proceedings. 16th International Conference
Keywords
Field
DocType
character recognition,hidden Markov models,pattern classification,sensor fusion,column features,data fusion,decision fusion,hidden Markov models,line column features,pattern classifier,printed character recognition,real joint log-likelihood
Markov process,Decision fusion,Pattern recognition,Character recognition,Markov model,Computer science,Sensor fusion,Speech recognition,Artificial intelligence,Classifier (linguistics),Hidden Markov model,Statistical assumption
Conference
Volume
ISSN
ISBN
3
1051-4651
0-7695-1695-X
Citations 
PageRank 
References 
7
0.59
6
Authors
3
Name
Order
Citations
PageRank
Khalid Hallouli170.59
Laurence Likforman-Sulem256043.90
Marc Sigelle331634.12