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|Title:||Model-Based Chart Image Recognition||Authors:||Huang, W.
|Issue Date:||2004||Citation:||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|
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