Paper Info
Title
A Novel Short Merged Off-line Handwritten Chinese Character String Segmentation Algorithm Using Hidden Markov Model
Abstract
Hidden Markov model (called "HMM"聺 for short) has been a widespread method to segment sequential data in speech recognition and DNA sequence analysis. According to the same principle, it can be also used in segmenting short merged off-line handwritten Chinese character strings, which is a tough issue but often met in practice. Because HMM is still not a common method in this field nowadays, in this paper, we will introduce a novel algorithm using HMM for the segmentation issue above. Eventually, this segmentation algorithm can achieve an applicable performance even when 3755 character classes are compressed into similar characters classes with only 1% amount of original ones, and it also shows an enormous potential of segmenting long text lines.
Year
DOI
Venue
2011
10.1109/ICDAR.2011.140
ICDAR-1
Keywords
Field
DocType
segmentation algorithm,handwritten chinese character string,segmentation issue,tough issue,common method,off-line handwritten chinese character,novel short merged off-line,character class,widespread method,dna sequence analysis,hidden markov model,novel algorithm,merging,hidden markov models,image segmentation,optical character recognition,handwriting recognition,hmm,algorithm design and analysis,decoding
Computer science,Handwriting recognition,Image segmentation,Artificial intelligence,String (computer science),Algorithm design,Pattern recognition,Segmentation,Optical character recognition,Algorithm,Speech recognition,Decoding methods,Hidden Markov model
Conference
ISSN
Citations 
PageRank 
1520-5363
5
0.41
References 
Authors
3
4
Name
Order
Citations
PageRank
Zhiwei Jiang1416.41
Xiaoqing Ding21219108.02
Changsong Liu335836.20
Yanwei Wang4283.46