Classifying chart images with sparse coding

We present an approach for classifying chart images with sparse coding. Three chart categories are considered: bar charts, pie charts and line graphs. We introduce the Laplacian of Gaussian (LoG) to smooth noise in the image and detect candidate regions of interest. Noting that charts typically contain both text and graphics, we identify text and graphic regions and learn informative features from them. Each image is then represented by a feature vector, which can be used to learn a sparse representation via the dictionary learning algorithm for classification. We evaluate the proposed systematic approach by a set of charts drawn from the internet. The encouraging results certifies the proposed method.