Model-Based Chart Image Recognition

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.