Paper Info
Title
Analyzing the information entropy of states to optimize the number of states in an HMM-based off-line handwritten Arabic word recognizer
Abstract
HMM is one of the most popular methods to model sequential signals and plays a significant role in the field of off-line handwritten Arabic word recognition research. However, the structure of an HMM including the number of states has to be determined initially and can hardly be updated during the training process. A novel analytic algorithm based on the information entropy of states in an HMM to optimize the number of states will be proposed in this paper. Information entropy is defined as an evaluation criterion of the activity of a state. According to principle of maximum entropy, states with minor information entropy do not possess so enough capability to represent actual observations that they should be deleted. Experiments on IFN/ENIT database show that the algorithm in this paper can bring approximately 3%-6% increase to correct recognition rate from the best performance of system with constant states.
Year
Venue
Keywords
2012
ICPR
hmm-based offline handwritten arabic word recognizer,training process,state activity,analytic algorithm,states optimization,maximum entropy principle,state information entropy,offline handwritten arabic word recognition research,ifn-enit database,sequential signal modelling,handwritten character recognition,evaluation criterion,natural language processing,entropy,hidden markov models
Field
DocType
ISSN
Off line,Maximum-entropy Markov model,Arabic,Pattern recognition,Computer science,Word recognition,Speech recognition,Artificial intelligence,Principle of maximum entropy,Hidden Markov model,Entropy (information theory)
Conference
1051-4651
ISBN
Citations 
PageRank 
978-1-4673-2216-4
1
0.35
References 
Authors
0
4
Name
Order
Citations
PageRank
Zhiwei Jiang1416.41
Xiaoqing Ding21219108.02
Liangrui Peng38017.67
Changsong Liu435836.20