Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39404
Title: Model-Based Chart Image Recognition
Authors: Huang, W. 
Tan, C.L. 
Leow, W.K. 
Issue Date: 2004
Source: Huang, W.,Tan, C.L.,Leow, W.K. (2004). Model-Based Chart Image Recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3088 : 87-99. ScholarBank@NUS Repository.
Abstract: In this paper, we introduce a system that aims at recognizing chart images using a model-based approach. First of all, basic chart models are designed for four different chart types based on their characteristics. In a chart model, basic object features and constraints between objects are defined. During the chart recognition, there are two levels of matching: feature level matching to locate basic objects and object level matching to fit in an existing chart model. After the type of a chart is determined, the next step is to do data interpretation and recover the electronic form of the chart image by examining the object attributes. Experiments were done using a set of testing images downloaded from the internet or scanned from books and papers. The results of type determination and the accuracies of the recovered data are reported. © Springer-Verlag 2004.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/39404
ISSN: 03029743
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

38
checked on Dec 15, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.