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.
CITATION STYLE
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. https://doi.org/10.1007/978-3-540-25977-0_8
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