Paper Info
Title
A model based detecting approach for feature extraction of off-line handwritten Chinese character recognition
Abstract
To simulate the cognitive ability of a human brain, especially thinking in images, a dynamic network composed of model based detectors for feature extraction of off-line handwritten Chinese character recognition (HCCR) is proposed. It is first argued that, according to noetic science, the methods of HCCR can be divided into two categories: thinking in images and thinking in logic. The former is particularly emphasized. A multilayer representation of an attributed semantic network for Chinese characters is given. After that, how a model based substructure detector makes stroke segmentation, detects points, strokes and their relationships, and extracts features is put forward. Finally, the dynamic association procedure of the model based detector network which is composed of 1700 substructure detectors is also explained. A Chinese character recognition system has been built based on this feature extraction approach
Year
DOI
Venue
1993
10.1109/ICDAR.1993.395610
ICDAR-1
Keywords
Field
DocType
model based detecting approach,semantic networks,substructure detector,thinking in images,attributed semantic network,multilayer perceptrons,dynamic network,dynamic association procedure,feature extraction,cognitive ability,stroke segmentation,optical character recognition,off-line handwritten chinese character recognition,handwriting recognition,multilayer representation,hccr,artificial intelligence,image segmentation,semantic network,logic,pattern recognition,detectors
Dynamic network analysis,Computer vision,Chinese characters,Pattern recognition,Computer science,Handwriting recognition,Optical character recognition,Feature extraction,Image segmentation,Semantic network,Artificial intelligence,Detector
Conference
ISBN
Citations 
PageRank 
0-8186-4960-7
4
0.82
References 
Authors
0
3
Name
Order
Citations
PageRank
Ju-Wei Tai1122.08
Ying-Jan Liu240.82
Liqin Zhang3696.56