Chart classification by combining deep convolutional networks and deep belief networks

Chart classification is the foundation of chart analysis and document understanding. In this paper, we propose a novel framework to classify charts by combining convolutional networks and deep belief networks. In the framework, we firstly extract deep hidden features of charts, which are taken from the fully-connected layer of deep convolutional networks. We then utilize deep belief networks to predict the labels of the charts based on their deep hidden features. The convolutional networks are initialized using a large number of natural images and fine-tuned using the chart images to prevent overfitting. Compared with previous methods using primitive feature extraction, the deep features give our framework better scalability and stability. We collect a 5-class chart dataset with more than 5000 images and show that the proposed framework outperforms existing methods greatly.