Paper Info
Title
Effective handwritten hangul recognition method based on the hierarchical stroke model matching
Abstract
This study defines three models based on the stroke for handwritten Hangul recognition. Those are trainable and not sensitive to variation which is frequently founded in handwritten Hangul. The first is stroke model which consists of 32 stroke models. It is a stochastic model of stroke which is fundamental of character. The second is grapheme model that is a stochastic model using composition of stroke models and the last is character model that is a stochastic model using relative locations between the grapheme models. This study also suggests a new stroke extraction method from a grapheme. This method does not need to define location of stroke, but it is effective in terms of numbers and kinds of stroke models extracted from graphemes of similar shape. The suggested models can be adapted to hierarchical bottom-up matching, that is the matching from stroke model to character model. As a result of experiment, we obtain 88.7% recognition rate of accuracy that is better than those of existing studies.
Year
DOI
Venue
2006
10.1007/11815921_47
SSPR/SPR
Keywords
Field
DocType
stroke model,suggested model,handwritten hangul recognition,recognition rate,new stroke extraction method,handwritten hangul,grapheme model,effective handwritten hangul recognition,character model,stochastic model,hierarchical stroke model matching,hierarchical bottom-up matching,bottom up
Hierarchical control system,Random graph,Grapheme,Computer science,Stroke,Phonetics,Speech recognition,Stochastic modelling,Parsing,Hangul
Conference
Volume
ISSN
ISBN
4109
0302-9743
3-540-37236-9
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Wontaek Seo1101.47
Beom-Joon Cho2424.38